Measuring Volume and Constituents of Cells

ABSTRACT

A method of determining a volume of a platelet includes: (a) illuminating the platelet with incident light at a plurality of illumination wavelengths; (b) obtaining at least one two-dimensional image of the platelet corresponding to each illumination wavelength; (c) for each illumination wavelength, determining a mean optical density and a maximum optical density for the platelet; (d) determining an area of the platelet; (e) for each illumination wavelength, determining a volume of the platelet; (f) for each illumination wavelength, determining an integrated optical density for the platelet; and (g) determining the volume of the platelet based on a weighted combination of the area of the platelet, the volumes of the platelet corresponding to each of the illumination wavelengths, and the integrated optical densities for the platelet corresponding to each of the illumination wavelengths.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) to thefollowing U.S. Provisional Patent Applications: 61/476,170, filed onApr. 15, 2011; 61/476,179, filed on Apr. 15, 2011; 61/510,614, filed onJul. 22, 2011; 61/510,710, filed on Jul. 22, 2011; and 61/589,672, filedon Jan. 23, 2012. The entire contents of each of the foregoingapplications are incorporated by reference herein.

TECHNICAL FIELD

This disclosure relates to measurement of the volume and constituents ofblood cells including, but not limited to, red blood cells, platelets,and white blood cells.

BACKGROUND

The volume of blood cells, such as a red blood cells (RBCs) orplatelets, is an important metric that can be used to determine otherphysiologically and therapeutically relevant quantities. For example,the mean cell volume measurement of a patient's red blood cells can beused to assess whether the patient suffers from anemia. Measurement ofblood cell constituents is another important metric that can be used fora variety of diagnostic purposes. For example, the mean cell hemoglobincontent of a patient's red blood cells also can be used to assesswhether a patient suffers from anemia. Such relevant quantities of cellvolumes and constituents such as hemoglobin can then be used for avariety of diagnostic purposes, including identifying disease conditionspresent in a patient, and evaluating possible therapeutic courses ofaction.

SUMMARY

In general, in a first aspect, the disclosure features methods ofdetermining a hemoglobin content value of a red blood cell, the methodsincluding: (a) illuminating the cell with incident light at a pluralityof illumination wavelengths; (b) obtaining at least one two-dimensionalimage of the cell corresponding to each illumination wavelength; (c) foreach illumination wavelength, determining a mean optical density and amaximum optical density for the cell; (d) determining an area of thecell; (e) for each illumination wavelength, determining a volume of thecell based on the area of the cell and the mean optical density andmaximum optical density for the cell corresponding to the illuminationwavelength; (f) for each illumination wavelength, determining anintegrated optical density for the cell based on the area of the celland the mean optical density for the cell corresponding to theillumination wavelength; and (g) determining the hemoglobin contentvalue of the cell based on a weighted combination of the area of thecell, the volumes of the cell corresponding to each of the illuminationwavelengths, and the integrated optical densities for the cellcorresponding to each of the illumination wavelengths.

In another aspect, the disclosure features methods of determining avolume of a platelet, the methods including: (a) illuminating theplatelet with incident light at a plurality of illumination wavelengths;(b) obtaining at least one two-dimensional image of the plateletcorresponding to each illumination wavelength; (c) for each illuminationwavelength, determining a mean optical density and a maximum opticaldensity for the platelet; (d) determining an area of the platelet; (e)for each illumination wavelength, determining a volume of the plateletbased on the area of the platelet and the mean optical density andmaximum optical density for the platelet corresponding to theillumination wavelength; (f) for each illumination wavelength,determining an integrated optical density for the platelet based on thearea of the platelet and the mean optical density for the plateletcorresponding to the illumination wavelength; and (g) determining thevolume of the platelet based on a weighted combination of the area ofthe platelet, the volumes of the platelet corresponding to each of theillumination wavelengths, and the integrated optical densities for theplatelet corresponding to each of the illumination wavelengths.

In a further aspect, the disclosure features methods of determining avolume of a cell, the methods including: (a) illuminating the cell withincident light at a plurality of illumination wavelengths; (b) obtainingat least one two-dimensional image of the cell corresponding to eachillumination wavelength; (c) for each illumination wavelength,determining a mean optical density and a maximum optical density for thecell; (d) determining an area of the cell; (e) for each illuminationwavelength, determining a volume of the cell based on the area of thecell and the mean optical density and maximum optical density for thecell corresponding to the illumination wavelength; (f) for eachillumination wavelength, determining an integrated optical density forthe cell based on the area of the cell and the mean optical density forthe cell corresponding to the illumination wavelength; and (g)determining the volume of the cell based on a weighted combination ofthe area of the cell, the volumes of the cell corresponding to each ofthe illumination wavelengths, and the integrated optical densities forthe cell corresponding to each of the illumination wavelengths.

In another aspect, the disclosure features systems for determining ahemoglobin content value of a red blood cell, the systems including anillumination source configured to illuminate the cell with incidentlight at a plurality of illumination wavelengths, a detector configuredto obtain at least one two-dimensional image of the cell correspondingto each illumination wavelength, and an electronic processor configuredto: (a) determine a mean optical density and a maximum optical densityfor the cell at each illumination wavelength; (b) determine an area ofthe cell; (c) for each illumination wavelength, determine a volume ofthe cell based on the area of the cell and the mean optical density andmaximum optical density for the cell corresponding to the illuminationwavelength; (d) for each illumination wavelength, determine anintegrated optical density for the cell based on the area of the celland the mean optical density for the cell corresponding to theillumination wavelength; and (e) determine the hemoglobin content valueof the cell based on a weighted combination of the area of the cell, thevolumes of the cell corresponding to each of the illuminationwavelengths, and the integrated optical densities for the cellcorresponding to each of the illumination wavelengths.

In a further aspect, the disclosure features systems for determining avolume of a platelet, the systems including an illumination sourceconfigured to illuminate the platelet with incident light at a pluralityof illumination wavelengths, a detector configured to obtain at leastone two-dimensional image of the platelet corresponding to eachillumination wavelength, and an electronic processor configured to: (a)determine a mean optical density and a maximum optical density for theplatelet at each illumination wavelength; (b) determine an area of theplatelet; (c) for each illumination wavelength, determine a volume ofthe platelet based on the area of the platelet and the mean opticaldensity and maximum optical density for the platelet corresponding tothe illumination wavelength; (d) for each illumination wavelength,determine an integrated optical density for the platelet based on thearea of the platelet and the mean optical density for the plateletcorresponding to the illumination wavelength; and (e) determine thevolume of the platelet based on a weighted combination of the area ofthe platelet, the volumes of the platelet corresponding to each of theillumination wavelengths, and the integrated optical densities for theplatelet corresponding to each of the illumination wavelengths.

In another aspect, the disclosure features systems for determining avolume of a cell, the systems including an illumination sourceconfigured to illuminate the cell with incident light at a plurality ofillumination wavelengths, a detector configured to obtain at least onetwo-dimensional image of the cell corresponding to each illuminationwavelength, and an electronic processor configured to: (a) determine amean optical density and a maximum optical density for the cell at eachillumination wavelength; (b) determine an area of the cell; (c) for eachillumination wavelength, determine a volume of the cell based on thearea of the cell and the mean optical density and maximum opticaldensity for the cell corresponding to the illumination wavelength; (d)for each illumination wavelength, determine an integrated opticaldensity for the cell based on the area of the cell and the mean opticaldensity for the cell corresponding to the illumination wavelength; and(e) determine the volume of the cell based on a weighted combination ofthe area of the cell, the volumes of the cell corresponding to each ofthe illumination wavelengths, and the integrated optical densities forthe cell corresponding to each of the illumination wavelengths.

In a further aspect, the disclosure features methods for determining amean cell volume for a blood sample, the methods including: illuminatingthe sample with incident light at a plurality of illuminationwavelengths and obtaining a two-dimensional image of the sample at eachof the plurality of illumination wavelengths; identifying a plurality ofcells that appear in each of the images; for each one of the pluralityof cells, determining an integrated optical density corresponding toeach of the plurality of illumination wavelengths; for each one of theplurality of cells, determining a cell volume based on the integratedoptical densities corresponding to each of the plurality of illuminationwavelengths; and determining the mean cell volume for the blood samplefrom the cell volumes for each one of the plurality of cells.

In another aspect, the disclosure features methods for determining amean platelet volume for a blood sample, the methods including:illuminating the sample with incident light at a plurality ofillumination wavelengths and obtaining a two-dimensional image of thesample at each of the plurality of illumination wavelengths; identifyinga plurality of platelets that appear in each of the images; for each oneof the plurality of platelets, determining an integrated optical densitycorresponding to each of the plurality of illumination wavelengths; foreach one of the plurality of platelets, determining a platelet volumebased on the integrated optical densities corresponding to each of theplurality of illumination wavelengths; and determining the mean plateletvolume for the blood sample from the cell volumes for each one of theplurality of cells.

In a further aspect, the disclosure features methods for determining amean cell hemoglobin value for a blood sample, the methods including:illuminating the sample with incident light at a plurality ofillumination wavelengths and obtaining a two-dimensional image of thesample at each of the plurality of illumination wavelengths; identifyinga plurality of cells that appear in each of the images; for each one ofthe plurality of cells, determining an integrated optical densitycorresponding to each of the plurality of illumination wavelengths; foreach one of the plurality of cells, determining a cell hemoglobin valuebased on the integrated optical densities corresponding to each of theplurality of illumination wavelengths; and determining the mean cellhemoglobin value for the blood sample from the cell hemoglobin valuesfor each one of the plurality of cells.

In another aspect, the disclosure features systems for determining amean cell volume for a blood sample, the systems including anillumination source configured to direct incident light at a pluralityof illumination wavelengths to the sample, a detector configured toobtain two-dimensional images of the sample at each of the plurality ofillumination wavelengths, and an electronic processor configured to:identify a plurality of cells that appear in each of the images; foreach one of the plurality of cells, determine an integrated opticaldensity corresponding to each of the plurality of illuminationwavelengths; for each one of the plurality of cells, determine a cellvolume based on the integrated optical densities corresponding to eachof the plurality of illumination wavelengths; and determine the meancell volume for the blood sample from the cell volumes for each one ofthe plurality of cells.

In a further aspect, the disclosure features systems for determining amean platelet volume for a blood sample, the systems including anillumination source configured to direct incident light at a pluralityof illumination wavelengths to the sample, a detector configured toobtain two-dimensional images of the sample at each of the plurality ofillumination wavelengths, and an electronic processor configured to:identify a plurality of platelets that appear in each of the images; foreach one of the plurality of platelets, determine an integrated opticaldensity corresponding to each of the plurality of illuminationwavelengths; for each one of the plurality of platelets, determine aplatelet volume based on the integrated optical densities correspondingto each of the plurality of illumination wavelengths; and determine themean platelet volume for the blood sample from the platelet volumes foreach one of the plurality of cells.

In another aspect, the disclosure features systems for determining amean cell hemoglobin value for a blood sample, the systems including anillumination source configured to direct incident light at a pluralityof illumination wavelengths to the sample, a detector configured toobtain two-dimensional images of the sample at each of the plurality ofillumination wavelengths, and an electronic processor configured to:identify a plurality of cells that appear in each of the images; foreach one of the plurality of cells, determine an integrated opticaldensity corresponding to each of the plurality of illuminationwavelengths; for each one of the plurality of cells, determine a cellhemoglobin value based on the integrated optical densities correspondingto each of the plurality of illumination wavelengths; and determine themean cell hemoglobin value for the blood sample from the cell volumesfor each one of the plurality of cells.

In a further aspect, the disclosure features computer readable storagedevices having encoded thereon computer readable instructions that, whenexecuted by a processor, cause the processor to: receive a plurality ofimages of a blood sample, each of the plurality of images correspondingto a different wavelength of illumination light incident on the sample;identify a plurality of cells that appear in each of the images; foreach one of the plurality of cells, determine an integrated opticaldensity corresponding to each of the plurality of illuminationwavelengths; for each one of the plurality of cells, determine a cellvolume based on the integrated optical densities corresponding to eachof the plurality of illumination wavelengths; and determine a mean cellvolume for the blood sample from the cell volumes for each one of theplurality of cells.

In another aspect, the disclosure features computer readable storagedevices having encoded thereon computer readable instructions that, whenexecuted by a processor, cause the processor to: receive a plurality ofimages of a blood sample, each of the plurality of images correspondingto a different wavelength of illumination light incident on the sample;identify a plurality of platelets that appear in each of the images; foreach one of the plurality of platelets, determine an integrated opticaldensity corresponding to each of the plurality of illuminationwavelengths; for each one of the plurality of platelets, determine aplatelet volume based on the integrated optical densities correspondingto each of the plurality of illumination wavelengths; and determine amean platelet volume for the blood sample from the cell volumes for eachone of the plurality of cells.

In a further aspect, the disclosure features computer readable storagedevices having encoded thereon computer readable instructions that, whenexecuted by a processor, cause the processor to: receive a plurality ofimages of a blood sample, each of the plurality of images correspondingto a different wavelength of illumination light incident on the sample;identify a plurality of cells that appear in each of the images; foreach one of the plurality of cells, determine an integrated opticaldensity corresponding to each of the plurality of illuminationwavelengths; for each one of the plurality of cells, determine a cellhemoglobin value based on the integrated optical densities correspondingto each of the plurality of illumination wavelengths; and determine amean cell hemoglobin value for the blood sample from the cell volumesfor each one of the plurality of cells.

Embodiments of the methods, systems, and devices can include any one ormore of the following features.

The methods can include repeating steps (a) through (g) for a pluralityof red blood cells from a sample of blood to determine hemoglobincontent values for each one of the plurality of red blood cells, anddetermining a mean cell hemoglobin value for the sample from thehemoglobin content values for each one of the plurality of red bloodcells.

The methods can include identifying a first set of pixels in each imageof the cell that corresponds to the cell. The methods can includeidentifying a second set of pixels in each image that corresponds to thecell by removing pixels from the first set of pixels that correspond toa perimeter region of the cell. The methods can include determining thearea of the cell based on the first set of pixels.

The methods can include determining a perimeter of the cell based on thefirst set of pixels, and excluding the cell from the determination ofthe mean cell hemoglobin value if a ratio of the perimeter squared tothe area exceeds a threshold value. The methods can include determininga convex hull of the cell, determining an area enclosed by the convexhull, and excluding the cell from the determination of the mean cellhemoglobin value if a ratio of the area enclosed by the convex hull tothe area of the cell exceeds a threshold value. The methods can includeexcluding the cell from the determination of the mean cell hemoglobinvalue if the area of the cell is outside a selected area range.

The methods can include determining the volume of the cell at eachillumination wavelength based on a ratio of the mean optical density tothe maximum optical density corresponding to the illuminationwavelength. The methods can include determining the volume of the cellat each illumination wavelength based on a ratio of the mean opticaldensity to the sum of the maximum optical density and a correctionfactor at the illumination wavelength. The methods can include adding anoffset value to the ratio of the mean optical density to the sum of themaximum optical density and the correction factor to determine thevolume of the cell at each illumination wavelength. The methods caninclude determining values of the correction factor and the offset valuefrom a reference set of blood samples.

The plurality of illumination wavelengths can include at least threeillumination wavelengths. The plurality of illumination wavelengths caninclude at least one wavelength between 575 nm and 600 nm, at least onewavelength between 525 nm and 570 nm, and at least one wavelengthbetween 400 nm and 475 nm. The plurality of illumination wavelengths caninclude at least four illumination wavelengths. The plurality ofillumination wavelengths can include at least one wavelength between 620nm and 750 nm, at least one wavelength between 575 nm and 600 nm, atleast one wavelength between 525 nm and 570 nm, and at least onewavelength between 400 nm and 475 nm.

Assessing an operating condition of an automated blood analysis devicecan include operating the device so that the device uses the methodsdisclosed herein to determine a mean cell hemoglobin value for a controlcomposition, and comparing a reference value of the mean cell hemoglobinfor the control composition to the determined value of the mean cellhemoglobin to assess the operating condition of the device. Assessingthe operating condition of the device can include determining adifference between the determined and reference values of the mean cellhemoglobin for the control composition, re-calibrating the device if thedifference exceeds a threshold value. Re-calibrating the device caninclude determining, from a reference set of blood samples, a pluralityof weight coefficients that are used to determine the hemoglobin contentvalue of the cell based on the area of the cell, the volumes of the cellcorresponding to each of the illumination wavelengths, and theintegrated optical densities for the cell corresponding to each of theillumination wavelengths.

The electronic processor can be configured to repeat steps (a) through(e) for a plurality of red blood cells from a sample of blood todetermine hemoglobin content values for each of the plurality of redblood cells, and determine a mean cell hemoglobin value for the samplefrom the hemoglobin content values for each of the plurality of redblood cells.

The systems can include an automated blood sample preparation system.

The methods can include repeating steps (a) through (g) for a pluralityof platelets or cells from a sample of blood to determine volumes foreach of the plurality of platelets or cells, and determining a meanplatelet volume or a mean cell volume for the sample from the volumesfor each of the plurality of platelets. The methods can includeidentifying a set of pixels in each image of the platelet or cell thatcorresponds to the platelet or cell. Identifying the set of pixels caninclude identifying a first set of pixels that corresponds to a centralregion of the platelet, identifying a second set of pixels thatcorresponds to a non-central region of the platelet, and merging thefirst and second sets of pixels to form the set of pixels thatcorresponds to the platelet. The methods can include identifying thesecond set of pixels based on intensity values for each member of theset of pixels in at least two images corresponding to differentillumination wavelengths.

The plurality of illumination wavelengths can include an illuminationwavelength in a blue region of the electromagnetic spectrum and anillumination wavelength in a yellow region of the electromagneticspectrum, and the methods can include obtaining an image of the plateletcorresponding to the illumination wavelength in the blue region and animage of the platelet corresponding to the illumination wavelength inthe yellow region. The methods can include excluding the platelet fromthe determination of the mean platelet volume if an integrated opticaldensity for the platelet in the image corresponding to the illuminationwavelength in the yellow region is larger than 600. The methods caninclude excluding the platelet from the determination of the meanplatelet volume if an integrated optical density for the platelet in theimage corresponding to the illumination wavelength in the blue region islarger than 200. The methods can include determining the area of theplatelet or cell based on the set of pixels.

The methods can include determining the volume of the platelet or cellat each illumination wavelength based on a ratio of the mean opticaldensity to the maximum optical density corresponding to the illuminationwavelength. The methods can include determining the volume of theplatelet or cell at each illumination wavelength based on a ratio of themean optical density to the sum of the maximum optical density and acorrection factor at the illumination wavelength. The methods caninclude adding an offset value to the ratio of the mean optical densityto the sum of the maximum optical density and the correction factor todetermine the volume of the platelet or cell at each illuminationwavelength.

Assessing an operating condition of an automated blood analysis devicecan include operating the device so that the device uses the methodsdisclosed herein to determine a mean platelet volume or mean cell volumefor a control composition, and comparing a reference value of the meanplatelet volume or the mean cell volume for the control composition tothe determined value of the mean platelet volume or the mean cell volumeto assess the operating condition of the device. Assessing the operatingcondition of the device can include determining a difference between thedetermined and reference values of the mean platelet volume or mean cellvolume for the control composition, and re-calibrating the device if thedifference exceeds a threshold value. Re-calibrating the device caninclude determining, from a reference set of blood samples, a pluralityof weight coefficients that are used to determine the volume of theplatelet or the volume of the cell based on the area of the platelet orcell, the volumes of the platelet or cell corresponding to each of theillumination wavelengths, and the integrated optical densities for theplatelet or cell corresponding to each of the illumination wavelengths.

The electronic processor can be configured to repeat steps (a) through(e) for a plurality of platelets or cells from a sample of blood todetermine volumes for each of the plurality of platelets or cells, anddetermine a mean platelet volume or a mean cell volume for the samplefrom the volumes for each of the plurality of platelets or cells.

The methods can include: for each one of the plurality of cells orplatelets, determining a volume corresponding to each of the pluralityof illumination wavelengths; and for each one of the plurality of cellsor platelets, determining the cell or platelet volume based on theintegrated optical densities and the volumes corresponding to each ofthe plurality of illumination wavelengths. The methods can include: foreach one of the plurality of cells or platelets, determining an area ofthe cell or platelet; and for each one of the plurality of cells orplatelets, determining the cell or platelet volume based on the area ofthe cell or platelet and the integrated optical densities correspondingto each of the plurality of illumination wavelengths. The methods caninclude: for each one of the plurality of cells or platelets,determining an area of the cell or platelet; and for each one of theplurality of cells or platelets, determining the cell or platelet volumebased on the area of the cell or platelet and the integrated opticaldensities and volumes corresponding to each of the plurality ofillumination wavelengths.

Each two-dimensional image can correspond to incident light that isreflected from, or transmitted by, the sample.

The perimeter region of the cell can have a thickness of 0.5 microns ormore.

The methods can include, for each one of the plurality of cells orplatelets, determining a convex hull of the cell or platelet,determining an area enclosed by the convex hull, and excluding the cellor platelet from the determination of the mean cell volume or meanplatelet volume if a ratio of the area enclosed by the convex hull to anarea of the cell or platelet exceeds a threshold value.

The methods can include, for each one of the plurality of cells orplatelets, excluding the cell or platelet from the determination of themean cell or platelet volume if the area of the cell or platelet isoutside a selected area range.

For each one of the plurality of cells or platelets, determining theintegrated optical density corresponding to each of the plurality ofillumination wavelengths can include determining an area of the cell orplatelet, determining for each of the plurality of illuminationwavelengths a mean optical density for the cell or platelet based acorresponding one of the images, and determining the integrated opticaldensity corresponding to each wavelength based on the area of the cellor platelet and the mean optical density for the cell or plateletcorresponding to the wavelength.

For each one of the plurality of cells or platelets, determining thevolume corresponding to each of the plurality of illuminationwavelengths can include: for each of the plurality of illuminationwavelengths, determining a mean optical density and a maximum opticaldensity for the cell or platelet; and for each of the plurality ofillumination wavelengths, determining the volume of the cell or plateletbased on a ratio of the mean optical density to the maximum opticaldensity corresponding to the wavelength.

For each one of the plurality of cells or platelets, determining thevolume corresponding to each of the plurality of illuminationwavelengths can include: for each of the plurality of illuminationwavelengths, determining a mean optical density and a maximum opticaldensity for the cell or platelet; and for each of the plurality ofillumination wavelengths, determining the volume of the cell or plateletbased on a ratio of the mean optical density to the sum of the maximumoptical density corresponding to the wavelength and a correction factorat the wavelength. The methods can include adding an offset value to theratio of the mean optical density to the sum of the maximum opticaldensity and the correction factor to determine the volume of the cell orplatelet at each illumination wavelength.

For each one of the plurality of cells or platelets, the cell orplatelet volume can be determined based on a weighted linear combinationof the integrated optical densities corresponding to each of theplurality of illumination wavelengths. For each one of the plurality ofcells or platelets, the cell or platelet volume can be determined basedon a weighted linear combination of the integrated optical densities andthe volumes corresponding to each of the plurality of illuminationwavelengths. For each one of the plurality of cells or platelets, thecell or platelet volume can be determined based on a weighted linearcombination of the area and the integrated optical densities and volumescorresponding to each of the plurality of illumination wavelengths. Themethods can include determining weight coefficients for the weightedlinear combination from a reference set of blood samples.

Re-calibrating the device can include determining, from a reference setof blood samples, a plurality of weight coefficients that are used todetermine the cell or platelet volume based on the integrated opticaldensities corresponding to each of the plurality of illuminationwavelengths.

The electronic processor can be configured to: for each one of theplurality of cells or platelets, determine a volume corresponding toeach of the plurality of illumination wavelengths; and for each one ofthe plurality of cells or platelets, determine the cell or plateletvolume based on the integrated optical densities and the volumescorresponding to each of the plurality of illumination wavelengths. Theelectronic processor can be configured to: for each one of the pluralityof cells or platelets, determine an area of the cell or platelet; andfor each one of the plurality of cells or platelets, determine the cellor platelet volume based on the area and the integrated opticaldensities corresponding to each of the plurality of illuminationwavelengths. The electronic processor can be configured to: for each oneof the plurality of cells or platelets, determine an area of the cell;and for each one of the plurality of cells or platelets, determine thecell or platelet volume based on the area and the integrated opticaldensities and volumes corresponding to each of the plurality ofillumination wavelengths.

Embodiments of the methods, systems, and devices can also include any ofthe other features and steps disclosed herein, as appropriate.

Although specific combinations of features and embodiments aredescribed, any of the features disclosed herein can be combined andsub-combined with any of the other features disclosed herein in themethods, systems, and devices, except where expressly precluded.Accordingly, it is to be understood that embodiments of the methods,systems, and devices disclosed herein can include any combination of thefeatures described in connection with any of the embodiments disclosedherein. Moreover, the embodiments of the methods, systems, and devicesdisclosed herein can include any combination of the features disclosedherein and any of the features disclosed in connection with any of theembodiments in U.S. Provisional Patent Applications: 61/476,170;61/476,179; 61/510,614; 61/510,710; and 61/589,672.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although methods and materialssimilar or equivalent to those described herein can be used in practiceor testing, suitable methods and materials are described below. Allpublications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including definitions, willcontrol. In addition, the materials, methods, and examples areillustrative only and not intended to be limiting.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description, drawings, and claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a schematic diagram of a red blood cell positioned on asubstrate.

FIG. 1B is a schematic plot showing transmitted light intensity as afunction of position for the cell of FIG. 1A.

FIG. 2 is a schematic diagram of a system for measuring volumes andconstituents of cells in a biological sample.

FIG. 3A is a schematic diagram showing a series of steps for determiningcellular metrics for cells in a biological sample.

FIG. 3B is a schematic diagram showing a series of steps for selecting arepresentative set of cells from one or more images of a biologicalsample.

FIG. 4 is a schematic image of a cell showing the cell boundary.

FIG. 5 is a schematic diagram showing two cells and convex hullsdetermined for each of the cells.

FIG. 6 is a schematic image of a cell showing variations in opticaldensity among cell pixels.

FIG. 7 is a schematic image of a blood sample.

FIG. 8 is a schematic image of the blood sample of FIG. 7 after applyinga threshold condition to the image.

FIGS. 9A and 9B are images of a blood sample.

FIG. 10 is a schematic diagram of an automated sample processing system.

FIG. 11 is a schematic diagram of a computing system for measuringvolume and constituents of cells.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Manual analysis of blood samples—which typically involves preparing ablood smear on a glass slide and then evaluating the smear under amicroscope—suffers from a number of disadvantages that make such methodsunsuitable for use in high-throughput environments. Human technicianpreparation of blood smears is prone to non-systematic errors,particularly the non-uniform distribution of blood throughout the smear.Frequently, certain regions of such smears are thicker than otherregions, making accurate quantitative analysis by a user difficultunless significant compromises are accepted (e.g., examining only asmall region of the overall smear, which region typically differs insize and location from smear to smear). Further, when stains are appliedto blood samples, variations in the staining protocol from one sample toanother can occur as a result of human error. These variations can, inturn, cause variations in quantitative measurements taken from thesamples that do not necessarily correspond to underlying variations inquantities of cellular constituents in the sample. Moreover, humantechnician preparation of individual blood smears is a time-consumingprocess, and can result in the use of large quantities of preparativesolutions (e.g., stains, rinsing solutions, buffer solutions,fixatives). Consumption of such solutions can be costly. Further, thegeneration of large volumes of these solutions also introducesdifficulties and costs related to the disposal of waste volumes of thesolutions.

Current automated sample preparation systems can significantly reducenon-systematic errors associated with manual preparation processes.Variations still exist, however, in prepared samples using such systems.For example, with a sample of blood prepared on a microscope slide,automated stainers may not uniformly stain the entire sample; dependingon sample thickness, sample drying time, and other variables associatedwith the sample preparation and staining process, the sample may exhibitportions with varying levels of stain concentrations in the cells.

Disclosed herein are methods and systems for automated measurement ofthe volumes of individual cells and the concentrations of cellularconstituents in cells of prepared biological samples. The methods andsystems can be used on samples that are manually prepared by a trainedhuman operator. In addition, the methods and systems can be used toanalyze samples prepared in an automated system. In this way, themethods and systems disclosed herein permit high-throughput,fully-automated analysis of a variety of biological samples extractedfrom patients.

By way of example, the present disclosure describes measuring thevolumes and determining the hemoglobin content of individual red bloodcells in a blood sample taken from a human patient. However, thedisclosure is not limited to such applications. In particular, themethods and systems disclosed herein can be used to measure, in anautomated fashion, volumes of a variety of different types of cellsincluding platelets. In addition, other cellular constituents such asproteins other than hemoglobin can be measured using the methods andsystems disclosed herein. Moreover, the samples to be analyzed need notbe from human patients; the methods and systems disclosed herein canalso be used on samples from animals, or on compositions designed tomimic whole blood to control, calibrate, and verify the linearity ofresults obtained from an automated hematology system.

Automated systems for measuring cells volumes and cell constituents fromprepared biological samples will be described in greater detail below.Once a sample is prepared, it is transported to the automatedmeasurement system. The measurement system acquires one or moretwo-dimensional images of the cells in the sample, and uses the imagesto determine, among other quantities, the volumes and hemoglobin contentof cells within the sample. The volumes and hemoglobin content of cellsare determined from information derived from images of the cellsobtained by directing incident light on the cells, and then detectingthe portion of the incident light that is either transmitted through, orreflected from, the cells. Each image is a two-dimensional image, wherean individual pixel intensity value within the image corresponds to theamount of transmitted or reflected light emerging from a spatiallocation on the cell that corresponds to the pixel.

General Considerations

The volume of any cell, such as a red blood cell or platelet, is athree-dimensional quantity. Determining the cell volume based oninformation derived from a two-dimensional image of the cell is one ofthe challenges that is addressed herein. Obtaining a measurement of thecell volume from a two-dimensional image involves estimating the shapeof the cell in the direction transverse to the two-dimensional plane ofthe image from information derived from the image. If all cells were thesame shape, determining the volume would be relatively straightforward:the volume of any such cell would be proportional to the cross-sectionalarea of the cell (which can be obtained from a two-dimensional image)raised to the 3/2 power. Cells are not uniformly shaped, however, so theabove assumption is not reliable in all instances or sufficientlyaccurate for diagnostic purposes.

As an example, red blood cells typically exhibit a variety of shapes:some are approximately spherical, while others have a shape that iscloser to toroidal. In addition, red blood cells have a centralindentation of variable depth. Images of such cells depict edge regionswhere the intensity of transmitted light is relatively smaller, and acentral region of increased light transmission referred to as the“central pallor.” FIG. 1A is a schematic diagram showing a red bloodcell 10 positioned on a substrate 20. The thickness of cell 10 along thex-direction varies; the thickness is largest near the edges of the celland reduced in the center region 15 of the cell. FIG. 1B is a schematicplot of transmitted light intensity as a function of position along thex-direction for a particular cross-sectional position through thetransmitted light distribution from cell 10. The intensity distributionshown in FIG. 1B includes a local intensity maximum 25 within thecentral pallor of the cell, and local intensity minima 27 and 28 oflower light transmission that corresponds to the edges of the cell. Evenunstained red blood cells will exhibit the phenomenon shown in FIG. 1Bbecause these cells contain hemoglobin, which absorbs blue light.

Cell 10 is typically prepared (as part of a sample) by applying one ormore stains to the cell to obtain the intensity distribution shown inFIG. 1B. The stain binds to the cell's cytoplasm, and serves as a markerfor the cytoplasm in cell images. When the cell is illuminated withincident light, the stain absorbs a portion of the incident light; theamount of absorption at a particular location in the sample depends onthe amount of stain present at that location. If it is assumed that theapplied stain binds in a uniform manner to all cytoplasm, then thetransmitted light intensity at a given image pixel should beproportional to the thickness of the cytoplasm at a correspondinglocation within the cell. As such, a quantity proportional to a cellvolume could be obtained by summing contributions to the transmittedlight intensity from all pixels within an image that correspond to theparticular cell. In practice, however, prepared samples of blood cellsexhibit some variability in the extent that applied stains uniformlybind to all cytoplasm.

The amount of transmitted light at each pixel location, however, alsodepends on amounts of various sample constituents—such as hemoglobin inred blood cells—present at each location in a cell. Further, thedistribution of stain from one spatial location to the next also affectsthe amount of measured transmitted light at each pixel location for agiven cell or blood constituent.

In view of these considerations, the methods and systems disclosedherein are adapted to determine cell volume based on information derivedfrom two-dimensional cell images by decoupling the estimate of cellthickness from the absorptive effects of locally-varying concentrationsof cellular constituents (e.g., hemoglobin) and various applied stains.To implement such decoupling, pixel intensities are scaled according tothe maximum pixel intensity for each cell. As further described below,cell volume calculations are based on a weighted combination of opticaldensity values for each color of illumination used to acquire cellimages, and cell area. The processes described herein can be repeatedfor each member of a set of cells selected for volume measurement, andthe results for each cell can be used to calculate a mean cell volumefor the sample.

Concentrations of cellular constituents, such as hemoglobin content ofred blood cells, also can be determined from calculations based onoptical density values for each color of illumination used to acquirecell images, as will be described further below, and the followingdisclosure also encompasses methods and systems for automatedmeasurement of one or more constituents of cells from preparedbiological samples. By way of example, the present disclosure focuses onthe measurement of hemoglobin in red blood cells in a blood sample takenfrom a human patient. However, it is to be understood that thedisclosure is not limited to such applications. In particular, themethods and systems disclosed herein can be used to measure, inautomated fashion, a variety of different constituents in a variety ofdifferent types of cells. Moreover, cell samples need not be taken fromhuman patients; the methods and systems disclosed herein can also beused on samples taken from animals, or on compositions manufactured tomimic whole blood, which are typically used to verify the performance ofan automated measurement system.

Conventional automated measurement systems, such as flow cytometers,typically determine cellular hemoglobin content by lysing red bloodcells and measuring the absorbance of the lysed sample in solution, orby measuring light scattered from individual red blood cells in a flowstream. The methods and systems disclosed herein can be implementedwithout lysing of any component of the blood sample or a flow cell.Rather, cellular constituents such as hemoglobin can be measured fromtwo-dimensional images of a sample deposited on a microscope slide,which preserves the natural morphology of the cells for othermeasurements and classification.

The sample preparation process for utilizing the automated methods andsystems described herein typically involves applying multiple stains tothe sample. The applied stains bind differently to different chemicaland/or structural entities within the sample, permitting selectiveanalysis of different sample features (e.g., certain stains bindpreferentially to blood cell nuclei, while others bind to cellmembranes, and still others may bind preferentially to certainconstituents within the cytoplasm). In turn, the applied stains enableautomated systems to perform several measurements on the sample such asidentifying and counting red blood cells, white blood cells, andplatelets, and performing a five-part white blood cell differential.With multiple stains present in the samples, however, spectroscopicmeasurements performed on the samples can conceivably suffer frominterfering effects produced by the various stains. For example, at agiven measurement wavelength, multiple stains may be significantabsorbers of incident light. The methods and systems disclosed hereinaccount for the presence of multiple spectral contributors (e.g.,absorbing stains and/or cellular constituents) within a sample, anddetermine amounts of one or more contributors present in the cells basedon information measured at multiple wavelengths.

The amount of hemoglobin in red blood cells is a quantity of interest,as the cellular hemoglobin content can be used to calculate a variety ofblood-related quantities for a sample (e.g., mean cell hemoglobinconcentration, hematocrit) that are used by physicians for diagnosticpurposes. Hemoglobin absorbs incident light more strongly within certainregions of the electromagnetic spectrum than in others, and thereforehas a characteristic spectral signature. When one or more stains areapplied to a sample, the stain(s) also have characteristic spectralsignatures and absorb incident light more strongly at certainwavelengths than others. In many applications, both an analyte ofinterest such as hemoglobin and one or more applied stains may havenon-negligible absorption at particular wavelengths. In suchapplications, the amount of incident light transmitted through thesample at a particular wavelength is related to the amount of absorptionby hemoglobin, by the other stains applied to the sample, and by theother sample constituents.

For purposes of the following disclosure and certain examples, at leasttwo stains are assumed to be applied to the samples: eosin and azure.However, the methods and systems disclosed herein are not limited toapplication of only two stains or solely to eosin and azure. To thecontrary, the methods and systems are capable of performing measurementson samples to which fewer stains (e.g., one stain, or no stains) or morestains (e.g., a red stain comprising eosin and a blue stain comprisingazure and methylene blue, three or more stains, four or more stains,five or more stains) have been applied.

Automated Measurement Systems

FIG. 2 shows a schematic diagram of a system 100 (which can be part of alarger sample processing and analysis system) for performing automatedmeasurements of cell volumes and constituents of cells from a biologicalsample. System 100 includes an illumination source 102, a detector 106,and an electronic control system 108. Electronic control system 108 caninclude a display 110, a human interface unit 112, and an electronicprocessor 114. Electronic control system 108 is connected toillumination source 102 and to detector 106 via control lines 120 and122, respectively.

Assuming that a sample has been prepared (as will be discussed furtherbelow) for analysis, the prepared sample 104 (e.g., a blood sampledeposited on a microscope slide and subsequently fixed, stained, andrinsed) is positioned automatically in proximity to source 102. Source102 directs incident light 116 toward sample 104. A portion of theincident light passes through sample 104 as transmitted light 118 and isdetected by detector 106. Transmitted light 118 forms an image of sample104 on the active surface of detector 106; the detector captures theimage, and then transmits the image information to electronic controlsystem 108. In general, electronic control system 108 directs source 102to produce incident light 116, and also directs detector 106 to detectthe image of sample 104. Control system 108 can instruct source 102 touse different illumination wavelengths when detector 106 acquires imagesof sample 104 from transmitted light 118.

The process discussed above can be repeated on multiple images of sample104 if desired. Prior to acquiring a new image, electronic controlsystem 108 can change the wavelength of incident light 116 produced bysource 102. As such, the images of sample 104 each correspond todifferent wavelengths of incident light 116 and therefore, differentwavelengths of transmitted light 118. The process repeats until at leastenough information has been acquired to perform an accuratedetermination of the volume of cells in the sample or the amount of oneor more constituents in the sample. Typically, the amount of informationthat yields an accurate determination of the volume of cells in thesample or the amount of one or more constituents in the sample isdetermined during a calibration process. For example, the calibrationprocess can be used to determine (as further described below) thataccurate determination of the volume of cells in the sample and/or theamount of one or more constituents in the sample can be achieved whenthe number of sample images obtained is equal to or greater than thenumber of spectral contributors (e.g., absorbers) factored into theanalysis of the sample.

As an example, for a prepared sample comprising red blood cells thatinclude hemoglobin as a naturally-present absorber, and eosin and azureas applied stains (for a total of three spectral contributors), system100 can continue to acquire sample images until it has obtained imagesat a minimum of three different wavelengths. Additionalimages—corresponding to further different wavelengths—can also beobtained and used in the determination of cellular constituents and cellvolumes for the sample.

Illumination source 102 can include one source or a plurality of thesame or different sources for directing incident light to a sample. Insome embodiments, source 102 can include multiple light emittingelements such as diodes (LEDs), laser diodes, fluorescent lamps,incandescent lamps, and/or flashlamps. For example, source 102 caninclude four LEDs having output wavelengths in the red, yellow, green,and blue regions of the electromagnetic spectrum, respectively (e.g.,635, 598, 525, and 415 nm), or more generally, about 620 to 750 nm(red), about 575 to 600 nm (yellow), about 525 to 570 nm (green), andabout 400 to 475 nm (blue). In certain embodiments, source 102 caninclude one or more laser sources. Instead of having multiple lightemitters, in other embodiments, source 102 can include a singlebroadband emitter than can be configured to alter its output wavelength(e.g., under the control of electronic control system 108). For example,source 102 can include a broadband source (e.g., an incandescent lamp)coupled to a configurable filter system (e.g., a plurality ofmechanically adjustable filters, and/or a liquid-crystal-basedelectronically-adjustable filter) that produces a variable outputspectrum under the control of system 108. In general, source 102 doesnot output illumination light 116 at a single wavelength, but in a bandof wavelengths centered around a central wavelength (e.g., thewavelength of maximum intensity in the band). When the discussion hereinrefers to the wavelength of illumination light 116, this reference is tothe central wavelength of the illumination band.

Detector 106 can include a variety of different types of detectors. Insome embodiments, detector 106 includes a charge-coupled device (CCD).In certain embodiments, detector 106 can include photodiodes (e.g., atwo-dimensional photodiode array). In some embodiments, detector 106 caninclude other light-sensitive elements such as CMOS-based sensors and/orphotomultipliers. Detector 106 can also include one or more filteringelements, as described above in connection with source 102. In someembodiments, sample images corresponding to different wavelengths areobtained by illuminating sample 104 with illumination light 116 having arelatively broad distribution of wavelengths, and then filteringtransmitted light 118 to select only a portion of the transmitted lightcorresponding to a small band of the wavelengths. Filtering can beperformed on either or both the excitation side (e.g., in source 102)and the detection side (e.g., in detector 106) to ensure that imagesobtained using detector 106 each correspond to a specific distributionof light wavelengths with a particular central wavelength.

General Methodology

The systems and methods disclosed herein acquire images of cells in asample (e.g., a blood sample) and determine quantities such as cellvolume and the amounts of cellular constituents based on the images.FIG. 3A shows a flowchart 300 that includes various steps fordetermining these quantities. In a first step 302, a set ofrepresentative cells are selected. The images of the cells in therepresentative set are the ones that are used for subsequentdetermination of cellular quantities for the sample. In the next step304, various image features are determined based on the images of thecells in the representative set. As will be discussed below, imagefeatures can include intensity values that are measured directly by adetector, and other values that are calculated from image data. In step306, cellular metrics such as cell volume and the amount of cellularconstituents (e.g., hemoglobin) are calculated based on the imagefeatures determined in step 304. The process terminates at step 308.Each of the steps in flow chart 300 is described greater detail below.

(i) Selecting a Set of Representative Cells

Before calculating cellular metrics, the systems and methods disclosedherein identify a set of representative cells for further analysis froma plurality of cells in a prepared biological sample. For example and asfurther described below for blood samples, such systems and methods useoptical density measurements obtained from sample images to identify arepresentative set of red blood cells suitable for volume andconstituent analysis. This process typically involves differentiatingand excluding other cells types such as white blood cells and platelets,overlapped or misshapen red blood cells, and non-cellular artifacts anddebris from further analysis.

Utilizing images acquired via detector 106, intensity values for eachpixel in a sample image can be correlated to an optical density valueused in the selection of a representative set of cells and subsequentcell volume and constituent analysis. The transmitted light intensityT(x,y) at a given image pixel (x,y) is related to the absorptioncoefficient α and the path length ε(x,y) of the incident light throughthe portion of the sample corresponding to that pixel:

T(x,y)=10^(−α·ε(x,y))  (1)

For each pixel in an image, the ratio of the pixel intensity to themaximum possible pixel intensity (e.g., pixel intensity/255 at 8-bitresolution) represents the fraction of light transmitted at the spatiallocation of the pixel. The fraction of transmitted light can beexpressed in optical density (OD) units by taking the logarithm ofEquation (1):

OD(x,y)=−log(T)=α·ε(x,y)  (2)

This process can be repeated for each pixel in the sample image. In thisway, the optical density at each pixel in each image corresponds to thetotal amount (e.g., the product of the absorption coefficient and thethickness) of absorbing material in the sample at the locationcorresponding to the pixel.

FIG. 3B shows a flowchart 320 that includes a series of steps forselecting a set of representative red blood cells in a prepared sampleof blood. After acquiring images of the sample, electronic controlsystem 108 and, in particular electronic processor 114, processes theimage information to differentiate cells for inclusion in the set ofrepresentative red blood cells from the other cell types, cell clusters,and artifacts present in the sample.

First, in step 322 of FIG. 3, system 100 locates red blood cells in oneor more sample images for further processing. Red blood cells typicallyabsorb blue light (e.g., 415 nm) due to the presence of hemoglobin inthe cells. White blood cells, however, do not contain hemoglobin andtherefore do not absorb blue light in the same manner as red bloodcells. An image of the sample acquired under blue light can be used toidentify red blood cells; white blood cells in such images appearfaintly and distorted because these cells minimally absorb blue light,thereby reducing contributions to the image and typically making themunidentifiable.

In some embodiments, a thresholding step can be used to ensure thatsystem 100 identifies only red blood cells for further analysis. Forexample, system 100 can utilize only image pixels below an intensity (orgray) value of 160 (for images captured at 8-bit resolution). Otherintensity value thresholds ranging from 100 to 180 can be used toidentify red blood cells from the image, while excluding white bloodcells from further analysis.

Next, in step 324, system 100 identifies a set of pixels for each redblood cell in the sample image. A variety of different methods can beused to identify sets of pixels associated with the cells. For example,in some embodiments, system 100 performs the identification step using aconnected components labeling process. This process correlatesindividual pixels from the sample image to an object in the image. Forexample, any two pixels in the image not separated by a pixel assignedto the background are assigned to the same cell.

In addition, system 100 can exclude pixels positioned within a borderregion of a cell from certain measurements relating to the cell volumeand constituent analysis. In particular, red blood cells often havethick, dark borders due to the manner in which these cells refractillumination light, for example, as shown in FIG. 9. Optical densitiesfor these pixels are typically unreliable due to this refraction. Aftercompleting the connected components labeling process, system 100 canapply a pixel erosion mask to the identified cells to remove theoutermost n layers of pixels (e.g., the pixel(s) that correspond to theboundary region where refraction is greatest). In general, the pixelerosion mask can be selected to remove any number n of pixel layers(e.g., one pixel layer or more, two pixel layers or more, three pixellayers or more, four pixel layers or more, five pixel layers or more,six pixel layers or more, eight pixel layers or more, ten pixel layersor more) depending on the magnification of the image. It has beendetermined experimentally that a pixel erosion mask comprising theoutermost 0.5 μm for the red cell perimeter is generally suitable forsignificantly reducing erroneous contributions to the measurement ofcell volume and hemoglobin content for red blood cells where each pixelcorresponds to a portion of the cell that is 0.148 μm×0.148 μm.Utilizing the sets of pixels corrected by erosion masks, various cellfeatures can be measured, such as a mean and maximum optical density foreach cell, which contribute to the cell volume and constituent analysis.

In step 326, system 100 continues the process of identifying a set ofrepresentative red blood cells from the sample image(s) by confirmingthat the set contains only complete and normally shaped and sized redblood cells. In general, step 326 functions to discard partial cells,overlapping cells, cell clusters, platelets, and non-cellular artifactsfrom inclusion in the set of representative red blood cells. Forexample, cells that are either cut off by, or touching, the edge of theimage frame can be excluded from further analysis, thereby preventinginaccurate measurements. In addition, misshapen cells—which can exhibitvariations in the determined cell volume that are related to theirnon-standard shapes—can be excluded from the analysis. Further,measurement results obtained from overlapping cells, which can beunreliable when used for calculating cell volumes or constituentcontent, can be precluded from the set of representative cells. Forthese reasons, the shapes of each of the identified cells are checked instep 326, and misshapen and/or overlapping cells are excluded fromfurther analysis.

A variety of different methods can be used to check the shape of theidentified cells. For example, in some embodiments, the shape of eachcell can be checked by comparing the perimeter and the area of the cell.FIG. 4 shows a schematic diagram of such a comparison. In FIG. 4, a cell400 has been identified as a set of pixels in a sample image. The pixelscorresponding to the boundary of cell 400 are shaded lighter in FIG. 4than the interior pixels for purposes of demonstration—they do notnecessarily appear this way in the actual image. The area of cell 400can be determined by counting the number of pixels in the set.

The cell perimeter is determined from the boundary pixels using the setof pixels corresponding to cell 400. This can be accomplished byconnecting a line through the center of each perimeter pixel to create apolygon in the image and measuring the perimeter of the polygon. Theratio of this cell perimeter value squared to the cell area value (i.e.,the area of the polygon) is determined to check the shape of the cell.The value of this ratio is 4π for an ideal, perfectly circular cell. Thevalue of the ratio increases as the cell shape departs from a circularoutline. Using this criterion, cells with a ratio of the perimetersquared to the area, which exceeds the minimum value of 4π by athreshold amount or more, are excluded from further analysis. Typically,the threshold amount is a percentage of the minimum value of 4π (e.g.,5% or more, 10% or more, 15% or more, 20% or more, 25% or more).

In addition to excluding misshapen individual cells from furtheranalysis, the procedure discussed above can also exclude overlappingcells. In sample images, overlapping cells typically appear as large,misshapen individual cells (with variations in transmitted lightintensity due to the increased thickness of material through which theincident light propagates). Overlapping cells are generally identifiedas large single cells with irregular boundaries when analysis algorithmsare applied to such images. As such, when the comparison of the cellperimeter and area is performed, the ratio falls well beyond thethreshold for allowable variance from the ideal value, and theoverlapping cells are excluded.

Another method for checking the shape of identified cells utilizes theconvex hull of the polygonal representation of the cell outlinedescribed above and compares the area enclosed by the convex hull to thecell area determined from the image pixels. A high ratio of convex hullarea to cell area can be used to identify irregularly shaped cells andexclude such cells from further analysis. FIG. 5 is a schematic diagramthat includes two cells 500A and 500B. The perimeters of cells 500A and500B are marked as 502A and 502B, respectively, in FIG. 5. A convex hull504A is drawn around cell 500A, and a convex hull 504B is drawn aroundcell 500B. As shown in FIG. 5, the discrepancy between the convex hullarea and the cell area is greater for cell 500A than for cell 500B.Given the high degree of irregularity for cell 500A, cell 500A can beexcluded from the set of representative red blood cells.

In some embodiments, cell area measurements can be used in step 326 toexclude artifacts and overlapping cells from the set of representativeblood cells. For example, only cells with an area ranging from 35 squaremicrons to 65 square microns can be considered for red blood cell volumemeasurements. Imaged objects with an area less than 35 square micronsare typically not red blood cells, but artifacts, such as a speck ofdust in the sample. Similarly, imaged objects with an area greater than65 square microns are typically not red blood cells; such object mightcorrespond to a blob of stain or to several overlapping cells. While theforegoing example describes a 35 to 65 square micron area range, otherranges can be used to select red blood cells for measurement (e.g., 20square microns to 80 square microns), and the range can be scaled basedon the average cell size in the sample, thereby accounting forpatient-to-patient variability. It has been determined experimentallythat while the 35-to-65 square micron range can exclude some red bloodcells, such range is more effective at removing artifacts from thesample image as compared to the 20-to-80 square micron range.

Optical density values can be used to select the set of representativered blood cells in the sample. For example, if the mean optical densityvalue of an object imaged under blue light is too low, the object may bea white blood cell nucleus instead of a red blood cell. A mean opticaldensity threshold can be used (e.g., mean optical density less than orequal to 0.33) for images acquired using blue light to exclude whiteblood cells from the set of representative red blood cells for thesample (e.g., a cell with a mean optical density less than or equal to0.33 is likely to be a white blood cell). For images acquired under blueor yellow illumination, a mean optical density value for an objectexceeding a certain threshold (e.g., mean optical density greater thanor equal to 0.66) can be used to identify stacked, overlapping, and/orclustered red blood cells, which can be excluded from further analysis(e.g., a red blood cell with a mean optical density greater than orequal to 0.66 is likely to be overlapping another red blood cell).

The process shown in FIG. 3B terminates at step 328 with the finaldetermination of a set of representative cells for further analysis.

(ii) Determining Image Features

The systems and methods disclosed herein use combinations of imagefeatures to calculate cellular volume and constituent values. Thecombinations typically include (but are not limited to) linearcombinations of such image features that the inventors have discoveredyield accurate, reproducible results for a wide variety of samples.

Once a representative set of cells has been identified as describedabove, some or all of the features disclosed herein are calculated foreach cell in the representative set based on one or more images of thecell obtained by system 100. A first set of features that can becalculated for each cell is the color-specific integrated opticaldensity, IOD(c), which can be determined as follows:

IOD(c)=A·OD_(mean)(c)  (3)

where A is the area of the cell, and OD_(mean)(c) is the mean opticaldensity of pixels in the cell when the cell is illuminated with light ofcolor c. If images of a cell are obtained at different illuminationwavelengths, a value of IOD(c) can be calculated for a cell at eachillumination wavelength. FIG. 6 shows a schematic image, obtained withillumination light of color c, of a representative cell 600 identifiedthrough the process described in connection with flowchart 320. Theimage of cell 600 includes a plurality of pixels. The mean opticaldensity of pixels in cell 600, OD_(mean)(c), corresponds to the sum ofthe pixel intensities in FIG. 6 divided by the number of pixels in theimage.

A second set of features that can be calculated for each cell in therepresentative set is the color-specific volume of the cell, Vol(c). Thevolume of cell 600 in FIG. 6 is calculated by summing optical densityvalues for each of the pixels that correspond to cell 600. First, the“height” of cell 600 at each pixel can be estimated as:

$\begin{matrix}{{height} = \frac{{OD}_{pixel}}{{OD}_{\max}}} & (4)\end{matrix}$

where OD_(pixel) is the optical density associated with the given pixel,and OD_(max) is the maximum optical density among all optical densitiesassociated with the cell pixels. Thus, for example, pixel 620 in theimage of cell 600 has an optical density that is smaller than themaximum optical density associated with pixel 610. The contribution ofpixel 620 to the volume of cell 600 is the ratio OD₆₂₀/OD_(max), whereOD₆₂₀ is the optical density of pixel 620 and OD_(max) is the opticaldensity of pixel 610. Then, the cell volume, V, is calculated by summingthe ratio of pixel optical density to maximum optical density for allpixels in cell 600:

$\begin{matrix}{V = {{\sum\limits_{pixels}\; \frac{{OD}_{pixel}}{{OD}_{\max}}} = {\frac{\sum\limits_{pixels}\; {OD}_{pixel}}{{OD}_{\max}} = \frac{N_{pixels} \cdot {OD}_{mean}}{{OD}_{\max}}}}} & (5)\end{matrix}$

where the sum of the optical densities associated with each of thepixels in cell 600 is replaced in Equation (5) by the product of thenumber of pixels in cell 600, N_(pixels), and the mean pixel opticaldensity for the pixels in cell 600, OD_(mean).

Typically, the optical density for pixels near the edge of a cell is nota valid contributor to the volume measurement because light refracted atthe edge of the cell creates an artificially dark border around thecell. To avoid this effect from such border pixels, the system can erodethe mask at the cell periphery by one or more pixels as previouslydescribed, measure the mean optical density and the maximum opticaldensity of the masked region of the cell, and, thereafter, extrapolateto the edge of the cell by multiplying by the area of the full,non-eroded mask.

Further, when multiple images corresponding to different illuminationwavelengths are used to obtain images of a single cell, a cell volumecalculation determination can be made at each color of illuminationlight. Accordingly, the color-specific cell volume can be determined as:

$\begin{matrix}{{{Vol}(c)} = \frac{A \cdot {{OD}_{mean}(c)}}{{OD}_{\max}(c)}} & (6)\end{matrix}$

where A is the area of the entire cell including the cell periphery,OD_(mean)(c) is the color-specific mean optical density for pixelswithin the masked region of the cell, and OD_(max)(c) is thecolor-specific maximum optical density for the eroded mask of the cell(e.g., pixel 610 in FIG. 6). The calculated color-specific cell volumeVol(c) can be scaled to express the cell volume in appropriate units(e.g., femtoliters).

In some embodiments, it is useful to add one or more correction factorsto Equation (6) to adjust for the fact that some of the darkness of acell image may not truly be due to the hemoglobin content of the cell.In addition, a scaling factor can be applied to convert the volumemeasurement to a unit of measurement such as femtoliters (fL). Toaccount for these correction and scaling factors, Equation (6) can berewritten as:

$\begin{matrix}{{{Vol}(c)} = {\frac{S \cdot \left\lbrack {A \cdot {{OD}_{mean}(c)}} \right\rbrack}{{{OD}_{\max}(c)} + C} + B}} & (7)\end{matrix}$

where S corresponds to a scaling factor or slope, C corresponds to acorrection factor to account for bias in the determination of maximumoptical density, and B corresponds to an intercept value thatcorresponds to a global offset value.

The correction factor, scaling factor, and intercept value can bedetermined experimentally using a data set of known volume values formultiple blood samples processed on, for example, a calibrated flowcytometer. A slightly different set of correction factors will, ingeneral, provide the best result for each different sample, althoughcorrection factors can be determined based on the results across anentire data set. For example, for a data set containing known volumevalues for 1,000 blood samples, a correction factor that works best onaverage across the entire data set can be determined by selecting thecorrection factor that minimizes the sum of squared differences betweenmeasured and expected volume values across the entire data set. Ascaling factor can be determined across the entire data set by selectingthe scaling factor that best converts raw volume values to a desirablemeasurement unit such as femtoliters. The intercept value B can beselected for the data set to ensure that Equation (7) passes through theorigin when the data are presented on a two-dimensional plot. Thecorrection factor, scaling factor, and intercept value can be stored ina memory unit associated with electronic control system 108, andretrieved from memory when determining color-specific cell volumes asshown in Equation (7) for analysis of new samples.

Using Equation (3) and Equation (6) or (7), two features (e.g.,integrated optical density IOD(c) and volume Vol(c)) can be determinedfor each color of illumination light used to obtain sample images. Forexample, if four different colors of illumination light are used, atotal of eight different features can be determined for each cell in therepresentative set. In addition, as explained above, the area A of eachindividual cell can be determined from an image of the cell. Thecolor-specific integrated optical densities and cell volumes, and thecellular area, can then be used to calculate metrics for each cell.

(iii) Calculation of Cellular Metrics

Cellular metrics such as cell volume and cellular constituent amountscan be calculated based on weighted combinations of some or all of thefeatures calculated for representative cells disclosed above. Ingeneral, a metric M can be determined according to:

$\begin{matrix}{M = {{\sum\limits_{n}\; \left\lbrack {{\omega_{n,i} \cdot {{IOD}(n)}} + {\omega_{n,v} \cdot {{Vol}(n)}}} \right\rbrack} + {\omega_{a} \cdot A} + K}} & (8)\end{matrix}$

where n corresponds to each of the colors of illumination light used toobtain images of a representative cell, the ω_(n,i) values arecolor-specific weight coefficients for each of the color-specificintegrated optical densities IOD(n), the ω_(n,v) values arecolor-specific weight coefficients for each of the color-specificvolumes Vol(n), ω_(a) is a weight coefficient for the cell area A, and Kis an offset value. For example, when four different illuminationwavelengths are used to obtain images of cells (e.g., red=r, yellow=y,green=g, and blue=b), then the volume of a cell, V, can be determinedas:

$\begin{matrix}{V = {\omega_{r,i} + {{IOD}(r)} + {\omega_{y,i} \cdot {{IOD}(y)}} + {\omega_{g,i} \cdot {{IOD}(g)}} + {\omega_{b,i} \cdot {{IOD}(b)}} + {\omega_{r,v} \cdot {{Vol}(r)}} + {\omega_{y,v} \cdot {{Vol}(y)}} + {\omega_{g,v} \cdot {{Vol}(g)}} + {\omega_{b,v} \cdot {{Vol}(b)}} + {\omega_{a} \cdot A} + K}} & (9)\end{matrix}$

Amounts of cellular constituents can be determined in similar fashion.For example, a concentration of hemoglobin in a cell, H, can becalculated according to:

$\begin{matrix}{H = {\omega_{r,i} + {{IOD}(r)} + {\omega_{y,i} \cdot {{IOD}(y)}} + {\omega_{g,i} \cdot {{IOD}(g)}} + {\omega_{b,i} \cdot {{IOD}(b)}} + {\omega_{r,v} \cdot {{Vol}(r)}} + {\omega_{y,v} \cdot {{Vol}(y)}} + {\omega_{g,v} \cdot {{Vol}(g)}} + {\omega_{b,v} \cdot {{Vol}(b)}} + {\omega_{a} \cdot A} + K}} & (10)\end{matrix}$

The difference between Equations (9) and (10) above is in the values ofthe weight coefficients and the offset K. Using Equations (9) and (10),cell volumes and constituent amounts (e.g., the amount of hemoglobin)can be determined for multiple cells in the sample. The results can beaveraged to determine mean cell volume and mean concentrations ofconstituents (e.g., mean cell hemoglobin) for the sample. Thedetermination of cell volumes and the amount of cellular constituentsbased on weighted combinations of color-specific image features and cellarea has been observed to significantly improve the accuracy of suchmeasurements, as compared to volume and constituent measurements basedon single-color optical density values and cell area.

The weight coefficients associated with the color-specific features inEquation (8) can be determined based on available training data, forexample, by determining linear regression coefficients that map theexperimentally determined sample features onto training data comprisingknown volume and/or constituent concentration values for such samples.Using a linear regression approach to determine color-specific weightscan improve the accuracy of sample mean cell volume and mean constituentconcentration measurements by correcting for uncontrollable factors thatimpact the volume measurement such as cell-to-cell variability inmembrane thickness and stain absorption. After color-specific weightvalues have been determined from training data, the weight values can bestored and later retrieved from a storage unit (e.g., an electronicmemory unit) prior to analysis of each sample.

In general, a wide variety of different samples can be used to determineappropriate weight coefficients. To obtain highly reproducible results,it can be advantageous to use training data that span the entire rangeof values of quantities that are calculated. Further, if samples to beanalyzed include unusual morphological features such as cell clumps, itcan be advantageous to use training data that include representativesamples of such features.

As an example, after determining a set of weight coefficients from a setof training data for the determination of cell volume, Equation (9) canbe re-written as follows:

$\begin{matrix}{V = {\left( {- 4.04} \right) + {{IOD}(r)} + {8.49 \cdot {{IOD}(y)}} + {\left( {- 3.69} \right) \cdot {{IOD}(g)}} + {4.40 \cdot {{IOD}(b)}} + {4.68 \cdot {{Vol}(r)}} + {\left( {- 8.20} \right) \cdot {{Vol}(y)}} + {3.57 \cdot {{Vol}(g)}} + {0.0159 \cdot {{Vol}(b)}} + {\left( {- 0.0125} \right)\mspace{14mu} A} + 4.84}} & (11)\end{matrix}$

Similarly, after determining suitable weight coefficients from a set oftraining data for the determination of cell hemoglobin, Equation (10)can be re-written as:

$\begin{matrix}{H = {\left( {- 1.05} \right) + {{IOD}(r)} + {\left( {- 2.44} \right) \cdot {{IOD}(y)}} + {1.12 \cdot {{IOD}(g)}} + {2.15 \cdot {{IOD}(b)}} + {1.95 \cdot {{Vol}(r)}} + {\left( {- 0.112} \right) \cdot {{Vol}(y)}} + {\left( {- 1.27} \right) \cdot {{Vol}(g)}} + {0.457 \cdot {{Vol}(b)}} + {\left( {- 0.221} \right) \cdot A} + {- 5.73}}} & (12)\end{matrix}$

The systems and methods disclosed herein can be used to analyze bothwhole blood samples (e.g., samples taken from patients) and qualitycontrol compositions. The weight coefficients shown in Equations(9)-(12) can be used to analyze both whole blood samples and qualitycontrol compositions. Quality control compositions typically includevarious types of preserved mammalian blood cells, and are designed tomimic whole blood samples when processed on an automated hematologysystem.

Quality control compositions can be analyzed to assess the operatingcondition of a blood analysis device such as an automated hematologysystem that embodies and executes the methods disclosed herein. Forexample, to perform an assessment of a device, the device can be used toanalyze one or more control compositions multiple times. The analysisresults (e.g., the determination of quantities such as cell hemoglobin,cell volume, mean cell hemoglobin, and mean cell volume) from repeatedanalysis of the same control compositions can be compared to assess thelinearity of the results produced by the device.

In some embodiments, a device can be used to analyze controlcompositions to assess the accuracy of the results produced by thedevice. For example, results from analysis of control compositions bythe device (e.g., the determination of quantities such as cellhemoglobin, cell volume, mean cell hemoglobin, and mean cell volume) canbe compared to reference values of these quantities for the controlcompositions to assess the device's accuracy. If a difference betweenthe determined and reference values for one or more of these quantitiesexceeds a threshold value, the device can be re-calibrated.Re-calibration can include, for example, re-determining values of someor all of the weight coefficients in Equations (9)-(12) from referenceblood samples, as described herein.

In Equations (9)-(12), four colors of illumination light (red, yellow,green, and blue) are used to illuminate the sample, and integratedoptical densities and cell volumes are calculated from images thatcorrespond to each of these colors. The illumination wavelengths used tocalculate the color-specific integrated optical density values andvolumes can be, e.g., 635 nm, 598 nm, 525 nm, and 415 nm, although othervalues within the red, yellow, green, and blue regions of theelectromagnetic spectrum can be used in other embodiments. Moregenerally, different numbers of illumination wavelengths can be used,and images corresponding to each of the illumination wavelengths can beobtained and used to calculate color-specific values of integratedoptical density and/or cell volume. For example, in some embodiments,three different wavelengths of light are used to illuminate a sample. Incertain embodiments, more than four wavelengths (e.g., five wavelengths,six wavelengths, seven wavelengths, eight wavelengths, ten) ofillumination light can be used, and color-specific integrated opticaldensities, cell volumes, and weight coefficients can be determined atsome or all of the illumination wavelengths. In general, the wavelengthsof illumination light can be selected such that each images at each ofthe different wavelengths include different information about thesample. For example, if the sample includes three spectral contributors,three wavelengths of illumination light can be selected for use suchthat each of the three wavelengths is most strongly absorbed by adifferent one of the spectral contributors.

As discussed above, color-specific weight coefficients in Equation (8)can be determined by mapping linear regression coefficients forexperimentally determined features for a large number (e.g., 1,000) ofblood samples onto a training data set comprising known values of cellvolume and/or concentrations of various cellular constituents for suchsamples, obtained for example from a calibrated flow cytometry system.With changes to sample preparation parameters (e.g., modifications tostain compositions affecting the appearance of stained cells or otherfactors impacting how cells absorb stain such as the extent of sampledrying before fixing and staining), the process of determiningcolor-specific weights and an intercept value for Equation 8 can berepeated to ensure determination of accurate volume and cell constituentmeasurement values for a given set of sample preparation parameters.However, once sample preparation parameters have been optimized for aparticular sample preparation system, the experimentally derived weightcoefficients and other parameter values in Equation (8) will generateaccurate and reproducible measurements of cell volume and/or cellconstituent amounts.

In Equation (8), metric M is calculated as a weighted linear combinationof the color-specific integrated optical densities, the color-specificvolumes, and the cell area. Not all of these features are used todetermine values of metrics in all embodiments, however. For example, insome embodiments, a metric M can be calculated as a weighted combinationof only the color-specific integrated optical densities or thecolor-specific volumes. In certain embodiments, a metric M can becalculated as a weighted combination of the cell area and either thecolor-specific integrated optical densities or the color-specificvolumes. In some embodiments, a metric M can be calculated as a weightedlinear combination of the color-specific integrated optical densitiesand the color-specific volumes. In general, a suitable combination offeatures used to calculate a particular metric M can be determined usingreference samples for which values of the metric M are known.

When determining the amount of a particular constituent in sample cells,if only a single spectral contributor is present in the sample (e.g., anabsorptive contributor such as hemoglobin), than the total amount ofthat contributor present in a particular cell can be determined bysumming intensity contributions from each of the pixels in the imagethat correspond to the selected cell. As the intensity contributionscorrespond only to absorption by hemoglobin, only one sample image wouldbe needed to determine the total amount of hemoglobin present in thecell.

In practice, however, samples are typically prepared with one or morestains to assist a technologist or an automated imaging system toidentify, count and classify various cell types. With multiple spectralcontributors in the sample, the absorption at each illuminationwavelength is a combination of absorption due to each contributor in thesample; the total contribution at any wavelength for a particular cellstill corresponds to the sum of contributions at that wavelength fromeach of the pixels representing the cell. Thus, with spectralcontributors hemoglobin (H), eosin (E), and azure (A) present in thesample, and assuming that three images of the sample correspond toillumination light having central wavelengths in the yellow (y), green(g), and blue (b) regions of the electromagnetic spectrum, the opticaldensities OD at each of these three wavelengths for a particular cell(or for all pixels in the image that correspond to one or more cells)can be assumed to be a linear combination of the absorption due to eachof the spectral constituents at each wavelength:

OD(y)=H·α _(y,H) +E·α _(y,E) +A·α _(y,A)

OD(g)=H·α _(g,H) +E·α _(g,E) +A·α _(g,A)

OD(b)=H·α _(b,H) +E·α _(b,E) +A·α _(b,A)  (13)

where α_(i,j) is the absorption coefficient for spectral contributor j(e.g., hemoglobin H, eosin E, or azure A) at wavelength i (e.g., yellowy, green g, or blue b).

Each of the central wavelengths of light can be determined by passing aknown spectrum of light through the sample to the detector and measuringthe absorbance of the sample. For example, the detector can acquirethree images of the sample using an illumination source with narrowillumination spectra in the yellow, green, and blue regions,respectively. Where each spectral contributor has an absorption spectrumcontaining a local maximum, illumination sources can be selected suchthat the emission spectra correspond to or best approximate the spectralcontributor local maxima. For example, the blue illumination can beselected as the wavelength that corresponds to the peak absorbance ofhemoglobin in the sample (e.g., 415 nm). The yellow illumination can becorrelated to the wavelength that corresponds to the peak absorbance ofazure stain in the sample (e.g., 598 nm). Similarly, the greenillumination wavelength can be selected at the wavelength thatcorresponds to the peak absorbance of eosin stain in the sample (e.g.,525 nm). Additional illumination wavelengths can be selected tocorrelate with peak absorbance values for additional spectralcontributors in the sample.

The optical density quantities OD(y), OD(g), and OD(b) can be determinedfrom the image information, and the absorption coefficients α_(i,j) canbe obtained from literature sources or determined experimentally. Thus,the system of Equation (13) includes three unknowns—H, E, and A—and canbe solved to yield the amounts of each of these three spectralcontributors present in each particular cell, or collectively for allcells in the sample if the pixels selected for analysis collectivelycorrespond to all of the identified cells in the images.

Nonetheless, the methods and systems disclosed herein present a simpler,more efficient method for determining amounts of cellular constituents.As shown above, Equation (8)—with suitable weight coefficients—can beused to determine constituent amounts of only those constituents ofinterest, increasing the speed with which sample analysis can becompleted. Further, in complex samples where the number of spectralcontributors is not well known, it can be difficult to construct asystem of equations such as in Equation (13). Equation (8), however,permits amounts of specific cellular constituents to be determined evenif the presence of other spectral contributors in the cell is not wellestablished. Thus, while in some embodiments the spectral contributionsfrom hemoglobin, eosin, and azure in the system of Equation (13) can bedistinguished by obtaining images at three different illuminationwavelengths, using more than three features and/or more than threeillumination wavelengths as described herein to determine values of cellmetrics such as cell hemoglobin, cell volume, mean cell hemoglobin, andmean cell volume permits correction for other systematic andnon-systematic sources of error when measuring blood samples.

In addition to color-specific integrated optical densities and volumes,and cell area, other image features can be used to determine cellvolumes and/or amounts of cellular constituents. In some embodiments,for example, Equation (8) can include an extra term that corresponds tothe product of a cell's perimeter and a weigh coefficient. Anappropriate weighting factor can be determined for the cell perimeterterm from training data, as described above. More generally, a varietyof additional terms derived from cell images, with suitable weightcoefficients determined from training data, can be included in Equation(8). Such terms can include geometrical image features relating to themorphology of the cells and/or color-specific measurements of integratedoptical density and volume at more than three or four illuminationwavelengths. Without wishing to be bound by theory, the additional termsmay allow the fitting—which is performed simultaneously on referencesample information to determine values of all weighting factors—tocorrect for effects such as imaging aberrations, absorption from othersample components, and systemic measurement errors, that are not fullyaccounted for by the model of Equation (8). For example, it has beenfound that the inclusion of integrated optical density and cell volumeterms corresponding to a red illumination wavelength and a termcorresponding to cell area improves the accuracy of determination ofcell hemoglobin in many samples as compared to measurement techniquesthat do not use sample images acquired at a red illumination wavelengthor cell area measurements.

In general, the methods and systems disclosed herein can be used todetermine amounts of naturally present constituents in samples (e.g.,hemoglobin in red blood cells) and/or amounts of constituents that havebeen added to samples (e.g., stains that have been applied, and thatbind to cellular components). Further, in some embodiments, the methodsand systems disclosed herein can be used to determine the amounts ofmore than one constituent present in the sample. For example, byapplying suitable stains and/or selecting appropriate centralwavelengths for the sample images, amounts of two or more constituentscan be determined. Consider a sample that includes hemoglobin as anaturally-occurring absorbing constituent. The sample can be stainedwith two broadly absorptive stains S(1) and S(2), and with a third stainS(3) with a relatively narrow absorption band. S(3) selectively binds toa particular constituent of interest in cells such that measuring theamount of S(3) present yields a measurement of the constituent.

If the absorption spectra of hemoglobin and S(3) are sufficientlyseparated spectrally such that hemoglobin has significant absorption atonly wavelengths λ₁, λ₂, and λ₃ but not at λ₄, and S(3) has significantabsorption at only wavelengths λ₂, λ₃, and λ₄ but not at λ₁, thenassuming S(1) and S(2) have significant absorption at all fourwavelengths, the amount of cellular hemoglobin can be determinedaccording to the methods disclosed herein for measuring cell constituentamounts from images of the sample corresponding to illuminationwavelengths λ₁, λ₂, and λ₃, and the amount of S(3) can be determinedaccording to the same methods from images of the sample corresponding toillumination wavelengths λ₂, λ₃, and λ₄. These approaches can begeneralized further to larger numbers of constituents of interest, andlarger or smaller numbers of broadly absorptive spectral contributorssuch as S(1) and S(2).

Reporting of Results

In certain embodiments, the determined cell volumes, constituentamounts, mean cell volume, and mean constituent concentrations can bedisplayed to a system operator using, e.g., display 110. The results canbe displayed on a per cell basis, or as averaged results for the wholesample. Also, calculated numerical results (e.g., for individual cells)can be overlaid atop one or more images of the cells. In general, asystem operator can exercise control over the manner in which resultsare displayed using human interface unit 112 (e.g., a keyboard and/ormouse and/or any other input device). The system operator can alsoexercise control over any of the other parameters, conditions, andoptions associated with the methods disclosed herein through interfaceunit 112 and display 110.

One or more metrics can also be calculated from the mean cell volumeand/or mean cell hemoglobin measurements, and displayed in step 210. Insome embodiments, for example, red blood cell distribution width can becalculated and displayed for a human operator. In turn, the red celldistribution width can be used to calculate and display the possibilityof anisocytosis and/or anemia. In addition, mean cell hemoglobinmeasurements can be used with a hematocrit value for the sample tocalculate mean cell hemoglobin concentration.

Cell volume and constituent concentration measurements and/or metricscalculated therefrom can be stored along with sample images in anelectronic storage unit associated with control system 108. For example,this information can be stored in an electronic record associated withthe patient to whom sample 104 corresponds. Alternatively, or inaddition, the information can be transmitted to one or more physiciansor other treatment personnel. The information can be transmitted via anetwork (e.g., a computer network) to a computing device. Further, theinformation can be transmitted to a handheld device such as a mobilephone, and the transmission can include an alert or warning if themetrics fall outside a predetermined range of values.

Other Analytes

As explained above, this disclosure focuses for illustrative purposes onthe determination of cell volume and cell constituent amounts in redblood cells. However, the systems and methods disclosed herein can alsodetermine volumes and cell constituent amounts for other types of cells.In particular, Equation (8) can be used to determine values of cellularmetrics for a variety of different types of cells.

As an example, the systems and methods disclosed herein can use a linearcombination of color-specific optical density and volumemeasurements—with suitably determined weight coefficients—to compute aplatelet volume for platelets within a given blood sample. Measurementsof platelet volume within a sample can be averaged to yield ameasurement of mean platelet volume. As with the previously describedexample for calculating cell volume and constituent amounts in red bloodcells, images of the blood sample are acquired using multiplewavelengths of light. The sample images are then analyzed according tothe steps in flow chart 300 to yield measurements of platelet volumes.

The first step in flow chart 300 is the selection of a set ofrepresentative platelets in step 302. An example of a prepared bloodsample containing platelets is illustrated in FIGS. 9A and 9B. FIG. 9Ashows an image 1100 of a specimen of blood that includes a large clusteror clump of platelets 1120. As platelets can clump and form a packedcluster, additional image processing may be required to identify theindividual platelets within a clump or cluster for possible inclusion inthe set of representative platelets. An example of such processing isillustrated in FIG. 9B. Objects in the image are segmented to identifyindividual platelets within the cluster. As shown in the segmentedcluster 1220 of FIG. 9B, the individual platelets are identified byborders.

The segmentation process can proceed in three stages. In a first stage,central regions of the individual platelets are identified in eachimage. Platelet central regions are shown as the nearly-circular regionsin FIG. 9B. The platelet central regions appear as the darkest regionsof the platelets.

In the second stage, pixels in the image that may be part of a plateletare identified. Pixels can be determined as being part of a plateletbased on one or more threshold conditions on the value of a given pixel.In some embodiments, if the intensity value of a pixel is greater than120 in an image acquired at a blue illumination wavelength (e.g., 415nm), and the intensity value is also at least 30 levels higher than anintensity value of the same pixel in an image acquired at a greenillumination wavelength (e.g., 525 nm), the pixel is identified as beingpart of a platelet.

In the third stage, each of the pixels identified in the second stage asbeing part of a platelet is assigned to a platelet corresponding to theplatelet center that is nearest to the pixel. If the distance between aparticular pixel and its nearest platelet center exceeds a thresholdvalue, the pixel is not assigned to any platelet. This stage results inassignment of a set of pixels corresponding to each of the identifiedplatelets. Sets of pixels corresponding individual platelets aredepicted in FIG. 9B as irregularly encircling platelet central regions.

In some embodiments, the boundaries between platelets can be subjectedto morphological operations such as dilation. Dilation can be halted,for example, when platelet boundaries do not overlap. However, erosionof the pixel mask (e.g., the subset of pixels) corresponding to eachplatelet is typically not performed as described above in connectionwith red blood cells. Platelet boundaries are typically thinner and lessrefractive than those of red blood cells. As a result, plateletboundaries do not appear dark in images, in contrast to the appearanceof red blood cells, and typically erosion of the pixel maskscorresponding to individual platelets is not performed. With plateletboundaries identified and pixels assigned to a given platelet, the areaof each platelet within the representative set can be calculated.

In general, a variety of segmentation algorithms can be used to segmenta cluster of platelets into individual platelets. Examples of suchalgorithms are described in U.S. Pat. Nos. 7,689,038 and 7,881,532, theentire contents of each of which are incorporated herein by reference.

As described above in connection with red blood cells, additional stepscan also be performed to determine whether individual platelets shouldbe included in the set of representative platelets used in subsequentcalculations. Platelets that touch any of the edges of the images or areotherwise obscured in the images are removed from the set ofrepresentative platelets and not used in further calculations.

Applying integrated optical density thresholds can further refine theset of representative platelets. For example, platelets with anintegrated optical density value greater than 600 when illuminated withlight at a yellow illumination wavelength can be excluded from therepresentative platelet set; such objects are typically bigger than aplatelet and are often red blood cells. In addition, platelets with anintegrated optical density value greater than 200 when illuminated withlight at a blue illumination wavelength can be excluded from therepresentative platelet set; set; such objects are typically too dark tobe a platelet and often indicate dust or other debris in the sample.

In some embodiments, a classifier such as a linear discriminantclassifier is applied to identify platelets that have settled on top ofred blood cells within the sample. The classifier utilizes a combinationof multiple platelet candidate features (e.g., five or more, ten ormore, fifteen or more, etc.) relating to the shape, texture, and colorof platelet candidates to identify and exclude overlapped platelets fromthe set of representative platelets.

Next, in step 304, color-specific optical density and volume values forthe set of representative platelets are determined according toEquations (3) and (6) or (7). Then, in step 306, platelet metrics suchas platelet volume are calculated using the features determined in step304 according to Equation (8). As described above, the weightsassociated with the features can be determined based on training data,for example, by determining linear regression coefficients that map thefeatures onto training data comprising known platelet volume values formultiple blood samples (e.g., as reported by a calibrated flow cytometrysystem).

As an example, after determining a set of weight coefficients from a setof training data for the determination of cell volume, Equation (9) canbe re-written so that platelet volume (PV) can be determined as follows:

$\begin{matrix}{{PV} = {{\left( {- 0.0047} \right) \cdot {{IOD}(y)}} + {0.050 \cdot {{IOD}(g)}} + {0.082 \cdot {{IOD}(b)}} + {0.28 \cdot {{Vol}(y)}} + {\left( {- 0.15} \right) \cdot {{Vol}(g)}} + {\left( {- 0.031} \right) \cdot {{Vol}(b)}} + {\left( {- 0.058} \right)\mspace{14mu} A} + 4.0}} & (14)\end{matrix}$

As discussed above, the systems and methods disclosed herein can be usedto analyze both whole blood samples (e.g., samples taken from patients)and quality control compositions. The weight coefficients shown inEquation (9) and (14) can be used to analyze both whole blood samplesand quality control compositions. Quality control compositions can beanalyzed to assess the operating condition of a blood analysis devicesuch as an automated hematology system that embodies and executes themethods disclosed herein. For example, to perform an assessment of adevice, the device can be used to analyze one or more controlcompositions multiple times. The analysis results (e.g., thedetermination of quantities such as platelet volume and mean plateletvolume) from repeated analysis of the same control compositions can becompared to assess the linearity and/or accuracy of the results producedby the device, and can be used to determine whether re-calibration ofthe device is warranted.

Platelet volumes can be determined for all platelets within one or moresets of representative platelets to calculate a mean platelet volume(MPV) value for the sample. While other illumination wavelengths in theelectromagnetic spectrum are useful for calculating platelet volumes,the inventors have found that including color-specific features forplatelets imaged with a red illumination wavelength in Equation (8) didnot significantly increase the accuracy of calculating platelet volumesor sample MPV values.

The systems and methods disclosed herein can also be used to determineother cellular metrics for platelets. In particular, the systems andmethods can be used to determine amounts of constituents in plateletsusing Equation (8) with weight coefficients determined from referencesamples, as described above.

Automated Sample Preparation Systems

The systems and methods disclosed herein can be used with a variety ofdifferent automated sample preparation systems. FIG. 10 shows aschematic diagram of an embodiment of an automated sample preparationsystem 1000. System 1000 includes multiple sub-systems for storingsubstrates, depositing samples on substrates, inspecting samplesprepared on substrates, and storing prepared samples.

Substrate storage sub-system 1010 is configured to store substratesprior to the deposition of samples thereon. Substrates can include, forexample, microscope slides, coverslips, and similar planar, opticallytransparent substrates. The substrates can be formed from a variety ofdifferent amorphous or crystalline materials including various types ofglasses. Sub-system 1010 can include a manipulator that selectsindividual substrates from a storage container and transfers theselected substrates to sample deposition sub-system 1020.

Sample deposition sub-system 1020 deposits a selected quantity of asample of interest—such as a blood sample—onto a substrate. Sub-system1020 includes, in general, a variety of fluid transfer components (e.g.,pumps, fluid tubes, valves) configured to deposit the sample. The fluidtransfer components can also be configured to expose the substrate tosolutions of various types, including wash solutions, one or more stainsthat bind to the sample, fixing solutions, and buffer solutions.Sub-system 1020 can also feature fluid removal components (e.g., avacuum sub-system) and a drying apparatus to ensure that the sample isfixed to the substrate. A substrate manipulator can transfer thesubstrate supporting the sample to imaging sub-system 1030.

Inspection sub-system 1030 includes various components for obtainingimages of samples on substrates, and for analyzing the images todetermine information about the samples. For example, inspectionsub-system 1030 can include one or more light sources (e.g., lightemitting diodes, laser diodes, and/or lasers) for directing incidentlight to a sample. Imaging sub-system 1030 can also include an opticalapparatus (e.g., a microscope objective) for capturing transmittedand/or reflected light from a sample. A detector (e.g., a CCD detector)coupled to the optical apparatus can be configured to capture images ofthe sample. Information derived from analysis of the images of thesample can be stored on a variety of optical and/or electronic storagemedia for later retrieval and/or further analysis.

Following inspection, a substrate manipulator can transfer the substrateto storage sub-system 1040. Storage sub-system 1040 can label individualsubstrates, for example, with information relating to the source of thesample applied to the substrate, the time of analysis, and/or anyirregularities identified during analysis. Storage sub-system can alsostore processed substrates in multi-substrate racks, which can beremoved from system 1000 as they are filled with substrates.

As shown in FIG. 10, each of the various sub-systems of system 1000 canbe linked to a common electronic processor 1050. Processor 1050 can beconfigured to control the operation of each of the sub-systems of system1000 in automated fashion, with relatively little (or no) input from asystem operator. Results from the analysis of samples can be displayedon system display 1060 for a supervising operator. Interface 1070permits the operator to issue commands to system 1000 and to manuallyreview the automated analysis results.

Additional aspects and features of automated sample processing systemsare disclosed, for example, in U.S. patent application Ser. No.12/943,687, filed on Nov. 10, 2010, the entire contents of which areincorporated herein by reference.

Hardware and Software Implementation

The method steps and procedures described herein can be implemented inhardware or in software, or in a combination of both. In particular,electronic processor 114 can include software and/or hardwareinstructions to perform any of the methods discussed above. The methodscan be implemented in computer programs using standard programmingtechniques following the method steps and figures disclosed herein.Program code is applied to input data to perform the functions describedherein. The output information is applied to one or more output devicessuch as a printer, or a display device, or a web page on a computermonitor with access to a website, e.g., for remote monitoring.

Each program is preferably implemented in a high level procedural orobject oriented programming language to communicate with a processor.However, the programs can be implemented in assembly or machinelanguage, if desired. In any case, the language can be a compiled orinterpreted language. Each computer program can be stored on a storagemedium or device (e.g., an electronic memory) readable by the processor,for configuring and operating the processor to perform the proceduresdescribed herein.

FIG. 11 is a schematic diagram of a computer system 1300 that can beused to control the operations described in association with any of thecomputer-implemented methods described herein, according to oneembodiment. The system 1300 includes a processor 1310, a memory 1320, astorage device 1330, and an input/output device 1340. Each of thecomponents 1310, 1320, 1330, and 1340 are interconnected using a systembus 1350. The processor 1310 is capable of processing instructions forexecution within the system 1300. In one embodiment, the processor 1310is a single-threaded processor. In another embodiment, the processor1310 is a multi-threaded processor. The processor 1310 is capable ofprocessing instructions stored in the memory 1320 or on the storagedevice 1330 to display graphical information for a user interface on theinput/output device 1340. The processor 1310 can be substantiallysimilar to the processor 1050 described above with reference to FIG. 10.

The memory 1320 stores information within the system 1300. In someembodiments, the memory 1320 is a computer-readable medium. The memory1320 can include volatile memory and/or non-volatile memory.

The storage device 1330 is capable of providing mass storage for thesystem 1300. In general, the storage device 1330 can include anynon-transitory tangible media configured to store computer readableinstructions. In one embodiment, the storage device 1330 is acomputer-readable medium. In various different embodiments, the storagedevice 1330 may be a floppy disk device, a hard disk device, an opticaldisk device, or a tape device.

The input/output device 1340 provides input/output operations for thesystem 1300. In some embodiments, the input/output device 1340 includesa keyboard and/or pointing device. In some embodiments, the input/outputdevice 1340 includes a display unit for displaying graphical userinterfaces. In some embodiments, the input/output device 1340 includesone or more of the display 1060 and interface 1070 described above withreference to FIG. 10.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, or in combinations ofthem. The features can be implemented in a computer program producttangibly embodied in an information carrier, e.g., in a machine-readablestorage device, for execution by a programmable processor; and featurescan be performed by a programmable processor executing a program ofinstructions to perform functions of the described embodiments byoperating on input data and generating output. The described featurescan be implemented in one or more computer programs that are executableon a programmable system including at least one programmable processorcoupled to receive data and instructions from, and to transmit data andinstructions to, a data storage system, at least one input device, andat least one output device. A computer program includes a set ofinstructions that can be used, directly or indirectly, in a computer toperform a certain activity or bring about a certain result. A computerprogram can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

Various software architectures can be used for implementing the methodsand systems described in this application. For example, apublish/subscribe messaging pattern can be used in implementing themethods and systems described herein. In the case of publish/subscribemessaging, the system includes several hardware and software modulesthat communicate only via a messaging module. Each module can beconfigured to perform a specific function. For example, the system caninclude one or more of a hardware module, a camera module, and a focusmodule. The hardware module can send commands to the imaging hardwareimplementing auto-focus functions, which in turn triggers a camera toacquire images. In some embodiments, the hardware module can include thecontrol system 108 described above with reference to FIG. 2.

A camera module can receive images from the camera and determine cameraparameters such as shutter time or focus. Images can also be buffered inthe computer's memory before being processed by the camera module. Whenperforming the initial search for the tilt of the slide, the cameramodule can also send a message interrupting the hardware module when ithas seen enough images to determine the proper shutter time or focus. Insome embodiments, the camera module includes the detector 106 describedabove with reference to FIG. 2.

The system can also include a focus module that can be implemented assoftware, hardware or a combination of software and hardware. In someembodiments, the focus module examines all the frames in a stack andestimates how far the stack is from the ideal or ideal focal distance.The focus module can also be responsible for assigning a focus score toeach frame in a stack of images.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both.Computers include a processor for executing instructions and one or morememories for storing instructions and data. Generally, a computer willalso include, or be operatively coupled to communicate with, one or moremass storage devices for storing data files; such devices includemagnetic disks, such as internal hard disks and removable disks;magneto-optical disks; and optical disks. Storage devices suitable fortangibly embodying computer program instructions and data include allforms of non-volatile memory, including by way of example semiconductormemory devices, such as EPROM, EEPROM, and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, ASICs(application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a keyboard and a pointing device such as a mouse or atrackball by which the user can provide input to the computer.Alternatively, the computer can have no keyboard, mouse, or monitorattached and can be controlled remotely by another computer

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a LAN, a WAN, and thecomputers and networks forming the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The processor 1310 carries out instructions related to a computerprogram. The processor 1310 can include hardware such as logic gates,adders, multipliers and counters. The processor 1310 can further includea separate arithmetic logic unit (ALU) that performs arithmetic andlogical operations.

Other Embodiments

It is to be understood that the foregoing description is intended toillustrate and not limit the scope of the disclosure, which is definedby the scope of the appended claims. Other aspects, advantages, andmodifications are within the scope of the following claims. For example,although the foregoing description and the schematic diagram in FIG. 2discuss the measurement of transmitted light from a sample, the methodsand systems disclosed herein can also be used when images of the samplecorrespond to light reflected from the sample. Certain samples may benaturally reflective, or can be tagged with reflective markers, suchthat reflected light provides a convenient method for determiningamounts of cellular constituents and/or volume. In some embodiments,sample 104 can be positioned atop a substrate such as a microscope slidewith a reflective coating. The reflective coating can function to directonce-transmitted light back through the sample a second time, so thatthe measured “reflected” light actually corresponds to incident lightthat has twice been transmitted through the sample.

In general, the methods and systems disclosed herein can be used todetermine per-cell volumes, constituent content, and/or sample mean cellvolume or mean cell constituent content for a variety of differentsample types. For example, cell volumes and constituent contents such ashemoglobin or other proteins can be determined for samples that includecells from body fluids and tissues, including blood, bone marrow, urine,epithelial tissue, tumors, semen, spittle, and other tissues orcirculating or non-circulating biological fluids.

EXAMPLES Example 1

To evaluate the effectiveness of the systems and methods disclosedherein for determining a mean cell volume for a blood sample, a 1 μLsample of blood was deposited on a microscope slide to form a monolayerof red blood cells on the slide. The blood sample was deposited usingthe system disclosed in co-pending U.S. patent application Ser. No.12/430,885, the entire contents of which are incorporated herein byreference. The sample was then prepared using fixative, red stain (e.g.,comprising eosin Y), blue stain (e.g., comprising azure B and methyleneblue), and rinse formulations disclosed in co-pending U.S. PatentApplication Ser. No. 61/505,011 in an automated system of the typedisclosed in co-pending U.S. patent application Ser. No. 13/293,050, theentire contents of which are incorporated herein by reference. After theblood sample was fixed, stained, rinsed, and dried, an automatedtransport mechanism loaded the slide onto an automated stage at a lowmagnification (e.g., under a 10× objective lens) imaging station.

The automated transport mechanism positioned the sample between a lightsource (e.g., source 106) and a detector (e.g., detector 102). Highmagnification images of the sample were acquired at locationscorresponding to objects of interest. The detector was paired with a 50×objective lens and included a 640-by-480 pixel CCD sensor. The pixelsize for images acquired via the detector was 7.4 μm; the dimensions ofthe field of view were:

${width} = {{\left( {640\mspace{14mu} {{pixels} \cdot 7.4}\frac{\mu m}{pixel}} \right)\text{/}50} = {95\mspace{14mu} {\mu m}}}$${height} = {{\left( {480\mspace{14mu} {{pixels} \cdot 7.4}\frac{\mu m}{pixel}} \right)\text{/}50} = {71\mspace{14mu} {\mu m}}}$

The imaging system acquired several hundred images of the sample usingthe 50× objective lens. For each location on the sample, images wereacquired at four different colors of illumination light (635, 598, 525,and 415 nm). Each image typically included 100 or more red blood cellsand therefore, the high magnification images of the sample yieldedimages of 60,000 or more red blood cells. FIG. 7 shows a schematic image700 of a sample, acquired with blue illumination light (e.g., at awavelength of 415 nm), although more generally, other illuminationwavelengths (e.g., 635, 598, 525 nm) or combinations thereof can be usedto acquire images, identify red blood cells, and determine cell volumesand constituent contents. Image 700 includes multiple identified objects702A-J.

Red blood cells were located within image 700 by applying a thresholdcondition to image 700. Specifically, only pixels having an intensitygray level of 160 or less (for an image at 8-bit resolution) wereprocessed further, to exclude white blood cells from the red blood cellvolume analysis. FIG. 8 is a schematic image 800 of the same sample thatis shown in FIG. 7 after the threshold condition has been applied to theimage. Objects 802A-J are present in image 800.

A connected components labeling process was then performed to correlateindividual pixels in image 800 to objects 802A-J. After this process wascomplete, a pixel erosion mask was applied to each of the sets of pixelscorresponding to objects 802A-J to remove the pixels representing theoutermost 0.5 microns from the perimeter of each object. As disclosedabove, the pixel erosion reduces image artifacts that are due torefraction of light from cell boundaries; these artifacts, ifuncompensated, can lead to errors in cell volume determination.

Several features of each of objects 802A-J were measured, and themeasurements are summarized in Table 1. The known area per pixel (0.022square microns per pixel) was used to calculate an area for each ofobjects 802A-J. The area calculation included the entire area of thecell, including the portion of the area that was removed by the pixelerosion mask.

The roundness of objects 802A-J was also assessed by determining thedeviation of the cross-sectional shape of each object from the profileof a perfect circle. As disclosed above, the ratio of the square of theperimeter to the area for a perfect circle is 4π. The roundness columnin Table 1 reflects the square-perimeter-to-area ratio for each objectin image 800 divided by 4π. A perfectly circular object will have aroundness value of 1, and the roundness value departs from 1 as theshape of the object becomes increasingly non-circular.

Optical density values were also measured for objects 802A-J. The graylevel value for each pixel in each object was converted to an opticaldensity value using Equation (2). The optical density values weredetermined for only the image pixels that were within the eroded mask.In turn, these optical density values were used to calculate a meanoptical density and a maximum optical density value for each of theobjects. In addition, integrated optical density values were calculatedfrom the cell area and mean optical density values.

Objects 802A-J in FIG. 8 were each identified as possible red bloodcells. Before calculating cell volumes, however, a representative set ofred blood cells was selected from the group of objects 802A-J. To selectrepresentative red blood cells, the objects were evaluated with regardto their geometric and optical density properties to ensure they hadsuitable shapes and features corresponding to red blood cells.

In a first step, image 800 was scanned to determine whether any of theobjects were cut off or otherwise obscured by the edge of the image(e.g., objects only partially contained within the image). Objects 802Hand 802J were each cut-off by at the edge of image 800. Accordingly,objects 802H and 802J were excluded from further analysis; the resultsof this process are shown in Table 1 in the “Partial Cell” column.

The next step in the identification of the set of representative redblood cells was to determine whether the remaining objects (objects802A-G and 8021) should be included in the set. To eliminate artifactsand/or groups of overlapping cells from the representative set, objectswith an area larger than 65 square microns or an area smaller than 35square microns were excluded from further analysis. In image 800,objects 802A, 802B, and 802I each had an area larger than 65 squaremicrons (97, 76, and 103 square microns, respectively, as shown in Table1). Accordingly, these objects were eliminated from the set ofrepresentative red blood cells. The shape of object 802A in FIG. 8suggests this object corresponds to either a highly irregular singlecell or to multiple cells. Objects 802B and 802I each appear tocorrespond to several clustered or overlapping cells.

The optical density values of the remaining objects 802C-G were thenanalyzed to determine whether these objects should be included in theset of representative red blood cells. In particular, a mean minimumoptical density threshold value of 0.33 was applied to each object toexclude objects that corresponded to white blood cells. As shown inTable 1, object 800C had a mean optical density value of 0.29, whichsuggested that object 802C was not a red blood cell, but perhaps a whiteblood cell nucleus. A comparison of object 802C in image 800 andcorresponding object 702C in image 700 suggested that object 802Ccorresponded to a white blood cell nucleus, and was therefore properlyexcluded from the representative set by application of the opticaldensity threshold. Relative to image 700, the pixel intensity thresholdremoved contributions from the cytoplasm that were apparent in object702C, leaving only the nucleus of the white blood cell visible in object802C.

In addition, a mean maximum optical density threshold of 0.66 wasapplied to each of objects 802C-G to ensure that the objects did notcorrespond to multiple overlapping cells where the degree of overlap wassufficiently high such that the objects could not be rejected on thebasis of shape irregularity. The optical density value for object 802Fexceeded the threshold, and object 802F was therefore excluded from theset of representative red blood cells. A comparison of object 802F inFIG. 8 and corresponding object 702F in FIG. 7 suggested that object802F corresponded to two overlapping cells, and was therefore properlyexcluded.

As a result, the representative set of red blood cells was reduced toobjects 802D, 802E, and 802G. These representative red blood cells werethen used to calculate the mean cell volume for the blood sample. Thearea, OD_(mean), and OD_(max) values from Table 1 for each of theseobjects were used in Equation (5) to calculate the volume for eachobject. The results of these volume calculations are shown in the“Volume” column of Table 1. Applying a correction factor of 0.5 to theseVolume values expressed the cell volumes in femtoliters as shown in the“Volume (fL)” column in Table 1. The individual volumes for each ofthese cells were then used to calculate a mean cell volume for thesample, which was 11.36 fL. The foregoing process can be repeated for aplurality of image locations on the sample (e.g., several hundred ormore), yielding a mean cell volume calculation based on hundreds orthousands of representative red blood cells in the sample. In addition,the process described above can be repeated to calculate color-specificintegrated optical density, IOD(c), and color-specific cell volume, Vol(c), values from images acquired at a plurality of illuminationwavelengths. For example, using a total of four colors of illumination(635, 598, 525, and 415 nm), the color specific integrated opticaldensity and cell volume values can be used with Equation 8 to calculatea mean cell volume for the sample.

TABLE 1 Par- Vol- tial Round- Vol- ume Object Cell Area OD_(mean) IODness OD_(max) ume (fL) 802A No 97 0.73 70.81 1.31 1.34 N/A N/A 802B No76 0.42 31.92 1.05 0.52 N/A N/A 802C No 41 0.29 11.89 1.01 0.34 N/A N/A802D No 45 0.43 19.43 1.01 0.53 23.31 11.66 802E No 51 0.39 19.89 1.020.56 23.13 11.56 802F No 47 0.53 24.91 1.03 0.84 N/A N/A 802G No 42 0.4418.48 1.01 0.55 21.74 10.87 802H Yes N/A N/A N/A N/A N/A N/A N/A 802I No103 0.41 42.23 1.04 0.51 N/A N/A

Example 2

Table 2 illustrates an example implementation of Equation (11) tocalculate mean cell hemoglobin values for blood samples based, in part,on a set of known MCH values for such samples. Six blood samples wereprocessed using a known reference system, a calibrated automatedhematology system using fluorescent flow cytometry methods to calculatethe various parameters of a complete blood count including MCH. The“Reference MCH” column in Table 2 reports the mean cell hemoglobin valuein picograms for each sample processed using this reference system.

The six samples were then processed and imaged, including theidentification of sets of representative red blood cells for eachsample, in the same manner as described above. For each sample, thesystem calculated an integrated optical density value for each color ofillumination used to acquire images of the sample (i.e., yellow—635 nm;green—598 nm, and blue—415 nm). The integrated optical density valuesfor each sample based on images obtained using the yellow, green, andblue light are reported in the Table 2 columns marked IOD(y), IOD(g),and IOD(b), respectively.

For each sample, three optical density values corresponding to thesample illumination colors were used to calculate a preliminary cellhemoglobin value. Weight coefficients previously determined fromreference samples for each illumination wavelength were used in thefollowing version of Equation (8):

H=−0.16·IOD(y)+0.04·IOD(g)+2.1·IOD(b)

Preliminary cell hemoglobin values for each sample are reported in the“H” column of Table 2. The preliminary cell hemoglobin values for eachsample were then scaled to the corresponding mean cell hemoglobin valuesdetermined using the reference system. This scaling process includedperforming a regression analysis to correlate the preliminary cellhemoglobin values to the reported reference mean cell hemoglobin valuesfor each sample. The optimal correlation corresponded to scaling theyellow weighting factor and adding an intercept value to the equationabove that was used to calculate the preliminary cell hemoglobinmeasurements, as follows:

H=−0.17·IOD(y)+0.04·IOD(g)+2.1·IOD(b)+0.5

Utilizing the foregoing equation, mean cell hemoglobin values werecalculated for each sample, and are reported in the “Experimental MCH”column in Table 2. This equation could then be applied to new samplesprocessed on the experimental system to calculate MCH values, withoutprocessing such samples on the reference system.

TABLE 2 Refer- Experi- ence mental IOD(y) IOD(g) IOD(b) MCH H MCH Sample1 7.73882 17.5825 13.1197 27.5 27.01646 27.43907 Sample 2 7.7805318.2845 15.3606 32.2 31.74376 32.16595 Sample 3 7.2244 16.9506 14.836131.1 30.67793 31.10569 Sample 4 6.88931 15.4531 14.4238 30.2 29.8058130.23692 Sample 5 8.624 17.846 15.7571 32.8 32.42391 32.83767 Sample 66.93862 15.4893 13.0128 27.2 26.83627 27.26689

1. A method of determining a volume of a platelet, the methodcomprising: (a) illuminating the platelet with incident light at aplurality of illumination wavelengths; (b) obtaining at least onetwo-dimensional image of the platelet corresponding to each illuminationwavelength; (c) for each illumination wavelength, determining a meanoptical density and a maximum optical density for the platelet; (d)determining an area of the platelet; (e) for each illuminationwavelength, determining a volume of the platelet based on the area ofthe platelet and the mean optical density and maximum optical densityfor the platelet corresponding to the illumination wavelength; (f) foreach illumination wavelength, determining an integrated optical densityfor the platelet based on the area of the platelet and the mean opticaldensity for the platelet corresponding to the illumination wavelength;and (g) determining the volume of the platelet based on a weightedcombination of the area of the platelet, the volumes of the plateletcorresponding to each of the illumination wavelengths, and theintegrated optical densities for the platelet corresponding to each ofthe illumination wavelengths.
 2. The method of claim 1, furthercomprising: repeating steps (a) through (g) for a plurality of plateletsfrom a sample of blood to determine volumes for each of the plurality ofplatelets; and determining a mean platelet volume for the sample fromthe volumes for each of the plurality of platelets.
 3. The method ofclaim 1, further comprising identifying a set of pixels in each image ofthe platelet that corresponds to the platelet.
 4. The method of claim 3,wherein identifying the set of pixels comprises: identifying a first setof pixels that corresponds to a central region of the platelet;identifying a second set of pixels that corresponds to a non-centralregion of the platelet; and merging the first and second sets of pixelsto form the set of pixels that corresponds to the platelet.
 5. Themethod of claim 4, further comprising identifying the second set ofpixels based on intensity values for each member of the set of pixels inat least two images corresponding to different illumination wavelengths.6. The method of claim 2, wherein the plurality of illuminationwavelengths comprises an illumination wavelength in a blue region of theelectromagnetic spectrum and an illumination wavelength in a yellowregion of the electromagnetic spectrum, and the method comprisesobtaining an image of the platelet corresponding to the illuminationwavelength in the blue region and an image of the platelet correspondingto the illumination wavelength in the yellow region.
 7. The method ofclaim 6, further comprising excluding the platelet from thedetermination of the mean platelet volume if an integrated opticaldensity for the platelet in the image corresponding to the illuminationwavelength in the yellow region is larger than
 600. 8. The method ofclaim 6, further comprising excluding the platelet from thedetermination of the mean platelet volume if an integrated opticaldensity for the platelet in the image corresponding to the illuminationwavelength in the blue region is larger than
 200. 9. The method of claim3, further comprising determining the area of the platelet based on theset of pixels.
 10. The method of claim 1, further comprising determiningthe volume of the platelet at each illumination wavelength based on aratio of the mean optical density to the maximum optical densitycorresponding to the illumination wavelength.
 11. The method of claim 1,further comprising determining the volume of the platelet at eachillumination wavelength based on a ratio of the mean optical density tothe sum of the maximum optical density and a correction factor at theillumination wavelength.
 12. The method of claim 10, further comprisingadding an offset value to the ratio of the mean optical density to thesum of the maximum optical density and the correction factor todetermine the volume of the platelet at each illumination wavelength.13. The method of claim 12, further comprising determining values of thecorrection factor and the offset value from a reference set of bloodsamples.
 14. The method of claim 1, wherein the plurality ofillumination wavelengths comprises at least three illuminationwavelengths.
 15. The method of claim 14, wherein the plurality ofillumination wavelengths comprises at least one wavelength between 575nm and 600 nm, at least one wavelength between 525 nm and 570 nm, and atleast one wavelength between 400 nm and 475 nm.
 16. The method of claim1, wherein the plurality of illumination wavelengths comprises at leastfour illumination wavelengths.
 17. The method of claim 16, wherein theplurality of illumination wavelengths comprises at least one wavelengthbetween 620 nm and 750 nm, at least one wavelength between 575 nm and600 nm, at least one wavelength between 525 nm and 570 nm, and at leastone wavelength between 400 nm and 475 nm.
 18. A method of assessing anoperating condition of an automated blood analysis device, the methodcomprising: operating the device so that the device uses the method ofclaim 2 to determine a mean platelet volume for a control composition;and comparing a reference value of the mean platelet volume for thecontrol composition to the determined value of the mean platelet volumeto assess the operating condition of the device.
 19. The method of claim18, further comprising: determining a difference between the determinedand reference values of the mean platelet volume for the controlcomposition; and re-calibrating the device if the difference exceeds athreshold value.
 20. The method of claim 19, wherein re-calibrating thedevice comprises determining, from a reference set of blood samples, aplurality of weight coefficients that are used to determine the volumeof the platelet based on the area of the platelet, the volumes of theplatelet corresponding to each of the illumination wavelengths, and theintegrated optical densities for the platelet corresponding to each ofthe illumination wavelengths.
 21. A system for determining a volume of aplatelet, the system comprising: an illumination source configured toilluminate the platelet with incident light at a plurality ofillumination wavelengths; a detector configured to obtain at least onetwo-dimensional image of the platelet corresponding to each illuminationwavelength; and an electronic processor configured to: (a) determine amean optical density and a maximum optical density for the platelet ateach illumination wavelength; (b) determine an area of the platelet; (c)for each illumination wavelength, determine a volume of the plateletbased on the area of the platelet and the mean optical density andmaximum optical density for the platelet corresponding to theillumination wavelength; (d) for each illumination wavelength, determinean integrated optical density for the platelet based on the area of theplatelet and the mean optical density for the platelet corresponding tothe illumination wavelength; and (e) determine the volume of theplatelet based on a weighted combination of the area of the platelet,the volumes of the platelet corresponding to each of the illuminationwavelengths, and the integrated optical densities for the plateletcorresponding to each of the illumination wavelengths.
 22. The system ofclaim 21, wherein the electronic processor is further configured to:repeat steps (a) through (e) for a plurality of platelets from a sampleof blood to determine volumes for each of the plurality of platelets;and determine a mean platelet volume for the sample from the volumes foreach of the plurality of platelets.
 23. The system of claim 21, whereinthe plurality of illumination wavelengths comprises at least onewavelength between 575 nm and 600 nm, at least one wavelength between525 nm and 570 nm, and at least one wavelength between 400 nm and 475nm.
 24. The system of claim 21, further comprising an automated bloodsample preparation system.